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Trainable classifiers or pattern recognizers (e.g., adaline, perceptron)

Subclass of:

382 - Image analysis

382155000 - LEARNING SYSTEMS

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Class / Patent application numberDescriptionNumber of patent applications / Date published
382160000 Generating a standard by statistical analysis 21
382161000 Alphanumerics 14
Entries
DocumentTitleDate
20110176724Content-Aware Ranking for Visual Search - This document describes techniques that utilize a learning method to generate a ranking model for use in image search systems. The techniques leverage textual information and visual information simultaneously when generating the ranking model. The tools are further configured to apply the ranking model responsive to receiving an image search query.07-21-2011
20130028508SYSTEM AND METHOD FOR COMPUTING THE VISUAL PROFILE OF A PLACE - A system and method for computing a place profile are disclosed. The method includes providing a geographical definition of a place, retrieving a set of images based on the geographical place definition. With a classifier, image-level statistics for the retrieved images are generated. The classifier has been trained to generate image-level statistics for a finite set of classes, such as different activities. The image-level statistics are aggregated to generate a place profile for the defined place which may be displayed to a user who has provided information for generating the geographical definition or used in an application such as a recommender system or to generate a personal profile for the user.01-31-2013
20090196493Cognitive Memory And Auto-Associative Neural Network Based Search Engine For Computer And Network Located Images And Photographs - Designs for cognitive memory systems storing input data, images, or patterns, and retrieving it without knowledge of where stored when cognitive memory is prompted by query pattern that is related to sought stored pattern. Retrieval system of cognitive memory uses autoassociative neural networks and techniques for pre-processing query pattern to establish relationship between query pattern and sought stored pattern, to locate sought pattern, and to retrieve it and ancillary data. Cognitive memory, when connected to computer or information appliance introduces computational architecture that applies to systems and methods for navigation, location and recognition of objects in images, character recognition, facial recognition, medical analysis and diagnosis, video image analysis, and to photographic search engines that when prompted with a query photograph containing faces and objects will retrieve related photographs stored in computer or other information appliance, and will identify URL's of related photographs and documents stored on the World Wide Web.08-06-2009
20080260241Grouping writing regions of digital ink - A method for grouping writing regions of digital ink receives processed digital ink that comprises writing regions. A relationship can be generated between a plurality of the writing regions. A feature set can be determined that is associated with the plurality of the writing regions. The plurality of the writing regions can be grouped based on the feature set.10-23-2008
20080260240User interface for inputting two-dimensional structure for recognition - In embodiments consistent with the subject matter of this disclosure, a user may input one or more strokes as digital ink to a processing device. The processing device may produce and present a recognition result, which may include a misrecognized portion. A user may indicate a desire to correct the misrecognized portion and may further select one or more strokes of the misrecognized portion. The processing device may then present the one or more recognition alternates corresponding to the selected one or more strokes of the misrecognized portion. In some embodiments, the processing device may permit a user to rewrite the selected one or more strokes of the misrecognized portion with newly entered digital ink. Features, such as, rewriting and correction of the input digital ink may be discoverable in some embodiments.10-23-2008
20090123062Information processing apparatus, information processing method, and program - Disclosed herein is an information processing apparatus configured to classify time-series input data into N classes, including, a time-series feature quantity extracting section, N calculating sections, and a determination section.05-14-2009
20100046829IMAGE STYLIZATION USING SPARSE REPRESENTATION - A computer-implemented method that includes segmenting a training image into training image patches, where each training image patch is represented by a linear combination of dictionary image patches from an image dictionary, and each dictionary image patch has a sparse representation coefficient. The method includes segmenting a stylized training image into stylized training image patches, where each stylized training image patch is represented by a linear combination of stylized dictionary image patches from a stylized image dictionary, and each stylized dictionary image patch has a sparse representation coefficient. The method also includes training the image dictionary with the training image patches and the stylized image dictionary with the stylized training image patches in a substantially simultaneous manner. The sparse representation coefficient for each training image patch is substantially similar to the sparse representation coefficient for the corresponding stylized training image patch.02-25-2010
20130077856PROCESSES AND SYSTEMS FOR TRAINING MACHINE TYPESETS FOR CHARACTER RECOGNITION - Processes and systems for training machine vision systems for use with OCR algorithms to recognize characters. Such a process includes identifying characters to be recognized and individually generating at least a first set of templates for each of the characters. Each template comprises a grid of cells and is generated by selecting certain cells of the grid to define a pattern that correlates to a corresponding one of the characters. Information relating to the templates is then saved on media, from which the information can be subsequently retrieved to regenerate the templates. The templates can be used in an optical character recognition algorithm to recognize at least some of the characters contained in a marking.03-28-2013
20130077857PRINTER IMAGE LOG SYSTEM FOR DOCUMENT GATHERING AND RETENTION - A system and method for document image acquisition and retrieval find application in litigation for responding to discovery requests. The method includes receiving automatically acquired electronic image logs comprising image data and associated records for documents processed by a plurality of image output devices within an organization. When a request for document production is received, the image logs (and/or information extracted therefrom) are automatically filtered through at least one classifier trained to return documents responsive to the document request, and documents corresponding to the filtered out image logs are output. One of the filters may be configured for filtering out documents that include attorney-client exchanges.03-28-2013
20130039570USING EXTRACTED IMAGE TEXT - Methods, systems, and apparatus including computer program products for using extracted image text are provided. In one implementation, a computer-implemented method is provided. The method includes receiving an input of one or more image search terms and identifying keywords from the received one or more image search terms. The method also includes searching a collection of keywords including keywords extracted from image text, retrieving an image associated with extracted image text corresponding to one or more of the image search terms, and presenting the image.02-14-2013
20130039571METHOD AND SYSTEM FOR LEARNING A SAME-MATERIAL CONSTRAINT IN AN IMAGE - In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.02-14-2013
20130039572METHOD AND SYSTEM FOR LOW COMPLEXITY TRANSCODING OF IMAGES WITH NEAR OPTIMAL QUALITY - Method and system for low complexity transcoding of images with near optimal quality for display on a terminal are presented. Generating effective transcoding parameters prior to transcoding and retrieving the transcoding parameters based on the features of the input image and the characteristics of the terminal, an output image quality close to that produced by optimal quality transcoding is achieved. The processing time is much smaller in comparison to that required for optimal quality transcoding.02-14-2013
20120183206INTERACTIVE CONCEPT LEARNING IN IMAGE SEARCH - An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules.07-19-2012
20130044942EVENT DETECTION THROUGH PATTERN DISCOVERY - Events are classified through string pattern recognition. Text labels are assigned to image primitives in a time-ordered set of training images and to related time-ordered transactions in an associated training transaction log in a combined time-ordered training string of text labels as a function of image types. Transactions are labeled in a training transaction log with a transaction label, a training primitive image of a start of a transaction with a start image text label, a training primitive of an entry of a transaction into the log with an entry image text label, and a training primitive of a conclusion of a transaction with an ending image text label. Positive subset string patterns are discovered representing true events from the combined time-ordered training string of text labels, and negative subset string patterns defined by removing single transaction primitive labels from the positive subset string patterns.02-21-2013
20130083996Using Machine Learning to Improve Visual Comparison - In some embodiments, information associated with a first plurality of image pairs is received. Each image pair is assessed to detect visual pairwise differences that qualify as an error. A visual pairwise difference may be a difference with respect to at least one of position, size, color, or style. A prediction engine is trained based upon the assessed visual pairwise differences. Information associated with a second plurality of image pairs is received. Each of these image pairs comprises at least a portion of a visual end-user experience screen of an event-driven application executed in a client-tier environment. Each of these image pairs is assessed, using the prediction engine, to detect visual pairwise differences that qualify as an error. User feedback is received, indicating that at least one assessed pairwise difference should not have qualified as an error. The prediction engine is then re-trained based on the user feedback.04-04-2013
20130051662LEARNING APPARATUS, METHOD FOR CONTROLLING LEARNING APPARATUS, DETECTION APPARATUS, METHOD FOR CONTROLLING DETECTION APPARATUS AND STORAGE MEDIUM - A learning apparatus comprises a plurality of detection units configured to detect a part or whole of a target object in an image and output a plurality of detection results; an estimation unit configured to estimate a state of the target object based on at least one of the plurality of detection results; a classification unit configured to classify the image into a plurality of groups based on the state of the target object; and a weight calculation unit configured to calculate weight information on each of the plurality of detection units for each of the groups based on the detection results.02-28-2013
20130051661Using Human Intelligence Tasks for Precise Image Analysis - Described are systems, methods, computer programs, and user interfaces for image location, acquisition, analysis, and data correlation that uses human-in-the-loop processing, Human Intelligence Tasks (HIT), and/or or automated image processing. Results obtained using image analysis are correlated to non-spatial information useful for commerce and trade. For example, images of regions of interest of the earth are used to count items (e.g., cars in a store parking lot to predict store revenues), detect events (e.g., unloading of a container ship, or evaluating the completion of a construction project), or quantify items (e.g., the water level in a reservoir, the area of a farming plot).02-28-2013
20090304268System and Method for Parallelizing and Accelerating Learning Machine Training and Classification Using a Massively Parallel Accelerator - A method system for training an apparatus to recognize a pattern includes providing the apparatus with a host processor executing steps of a machine learning process; providing the apparatus with an accelerator including at least two processors; inputting training pattern data into the host processor; determining coefficient changes in the machine learning process with the host processor using the training pattern data; transferring the training data to the accelerator; determining kernel dot-products with the at least two processors of the accelerator using the training data; and transferring the dot-products back to the host processor.12-10-2009
20120219213Embedded Optical Flow Features - Aspects of the present invention include systems and methods for generating an optical flow-based feature. In embodiments, to extract an optical flow feature, the optical flow at sparse interest points is obtained, and Locality-constrained Linear Coding (LLC) is applied to the sparse interest points to embed each flow into a higher-dimensional code. In embodiments, for an image frame, the multiple codes are combined together using a weighted pooling that is related to the distribution of the optical flows in the image frame. In embodiments, the feature may be used in training models to detect actions, in trained models for action detection, or both.08-30-2012
20120219212FEATURE CONVERSION DEVICE, SIMILAR INFORMATION SEARCH APPARATUS PROVIDED THEREWITH, CODING PARAMETER GENERATION METHOD, AND COMPUTER PROGRAM - A bit code converter transforms a learning feature vector using a transformation matrix updated by a transformation matrix update unit, and converts the transformed learning feature vector into a bit code. When the transformation matrix update unit substitutes a substitution candidate for an element of the transformation matrix, a cost function calculator fixes the substitution candidate that minimizes a cost function as the element. The transformation matrix update unit selects the element while sequentially changing the elements, and the cost function calculator fixes the selected element every time the transformation matrix update unit selects the element, thereby finally fixing the optimum transformation matrix. A substitution candidate specifying unit specifies the substitution candidate such that a speed of transformation processing that the bit code converter performs using the transformation matrix using the transformation matrix is enhanced based on a constraint condition stored in a constraint condition storage unit.08-30-2012
20120219211Contextual Boost for Object Detection - Aspects of the present invention includes systems and methods for generating detection models that consider contextual information of an image patch and for using detection models that consider contextual information. In embodiments, a multi-scale image context descriptor is generated to represent the contextual cues in multiple parameters, such as spatial, scaling, and color spaces. In embodiments, a classification context is defined using the contextual features and is used in a contextual boost classification scheme. In embodiments, the contextual boost propagates contextual cues to larger coverage through iterations to improve the detection accuracy.08-30-2012
20120219210Multi-Scale, Perspective Context, and Cascade Features for Object Detection - Systems and methods for object detection are presented herein. Embodiments of the present invention utilizing a cascade feature, one or more features at different scales, one or more multi-scale features in combination with a perspective feature, or combinations thereof to detect an object of interest in an input image. In embodiments, the various features are used to train classifiers. In embodiments, the trained classifiers are used in detecting an object of interest in one or more input images.08-30-2012
20120219209Image Labeling with Global Parameters - Image labeling with global parameters is described. In an embodiment a pose estimation system executes automatic body part labeling. For example, the system may compute joint recognition or body part segmentation for a gaming application. In another example, the system may compute organ labels for a medical imaging application. In an example, at least one global parameter, for example body height is computed for each of the images to be labeled. In an example, the global parameter is used to modify an image labeling process. For example the global parameter may be used to modify the input image to a canonical scale. In another example, the global parameter may be used to adaptively modify previously stored parameters of the image labeling process. In an example, the previously stored parameters may be computed from a reduced set of training data.08-30-2012
20130058566INFORMATION PROCESSOR, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processor includes a detection unit detecting a photographic subject region of an image, a characteristic amount generation unit generating a characteristic amount including at least positional information of the photographic subject region for each of the detected photographic subject region, a combined characteristic amount generation unit generating a combined characteristic amount corresponding to the image by combining the characteristic amount generated for each of the photographic subject region, and an identification unit identifying a label corresponding to a combination of a photographic subject appearing in the image based on the generated combined characteristic amount.03-07-2013
20090092312Identifying Method and Storage Medium Having Program Stored Thereon - An identification processing that matches a preference of a user is carried out. An identifying method according to the present invention is an identifying method, in which learning is carried out using a learning sample and, based on a learning result, identification is performed as to whether or not a target of identification belongs to a certain class, including: extracting a learning sample belonging to the certain class and a learning sample not belonging to the certain class, displaying a plurality of the extracted learning samples arranged on a display section, as well as displaying a mark between the learning sample belonging to the certain class and the learning sample not belonging to the certain class, and displaying the mark between a different pair of the learning samples by moving a position of the mark in response to an instruction of a user, changing an attribute information that indicates a class to which the learning sample belongs in response to the position of the mark determined by the user, and identifying whether or not a target of identification belongs to the certain class based on a result of relearning using the learning sample of which the attribute information has been changed.04-09-2009
20130058567MULTISCALE MODULUS FILTER BANK AND APPLICATIONS TO PATTERN DETECTION, CLUSTERING, CLASSIFICATION AND REGISTRATION - A digital filter bank having a number J≧1 of stages is disclosed. For each integer j such that 1≦j≦J, the j-th stage includes a plurality of filtering units (03-07-2013
20120224764METHOD FOR COLOR RECOGNITION - A color recognition method is provided, including a color learning phase and a color recognition phase. The color learning phase further includes the steps of: preparing a plurality of color images printed on at least a sheet of paper; for each color image of the plurality of color images, obtaining the digital signals representing the red, green and blue components of the color; associating the digital signals with an audio file related to the color image; and storing the digital signals with associated audio file to a database in the storage device. The color recognition phase further includes the steps of: obtaining the digital signals representing the red, green and blue components of an color image printed on a sheet of paper; comparing the digital signals representing the color image to a pre-stored database in a storage device; if a match is found, playing the associated audio file.09-06-2012
20130064444DOCUMENT CLASSIFICATION USING MULTIPLE VIEWS - A training system, training method, and a system and method of use of a trained classification system are provided. A classifier may be trained with a first “cheap” view but not using a second “costly” view of each of the training samples, which is not available at test time. The two views of samples are each defined in a respective original feature space. An embedding function is learned for embedding at least the first view of the training samples into a common feature space in which the second view can also be embedded or is the same as the second view original feature space. Labeled training samples (first view only) for training the classifier are embedded into the common feature space using the learned embedding function. The trained classifier can be used to predict labels for test samples for which the first view has been embedded in the common feature space with the embedding function.03-14-2013
20130163860Information Processing Device, Information Processing Method and Program - The present invention relates to an information processing device, an information processing method, and a program capable of easily adding an annotation to content.06-27-2013
20120170834Determining the Uniqueness of a Model for Machine Vision - Described are methods and apparatuses, including computer program products, for determining model uniqueness with a quality metric of a model of an object in a machine vision application. Determining uniqueness involves receiving a training image, generating a model of an object based on the training image, generating a modified training image based on the training image, determining a set of poses that represent possible instances of the model in the modified training image, and computing a quality metric of the model based on an evaluation of the set of poses with respect to the modified training image.07-05-2012
20080232681OBJECT DETECTION SYSTEM BASED ON A POOL OF ADAPTIVE FEATURES - A method, system and computer program product for detecting presence of an object in an image are disclosed. According to an embodiment, a method for detecting a presence of an object in an image comprises: receiving multiple training image samples; determining a set of adaptive features for each training image sample, the set of adaptive features matching the local structure of each training image sample; integrating the sets of adaptive features of the multiple training image samples to generate an adaptive feature pool; determining a general feature based on the adaptive feature pool; and examining the image using a classifier determined based on the general feature to detect the presence of the object.09-25-2008
20110280474AUTO CLASSIFYING IMAGES AS "IMAGE NOT AVAILABLE" IMAGES - An image may be accepted from a vendor, and the image may be submitted to an image analysis system. The image analysis system may determine whether the image is a not found image or a true image. The determination may occur in a variety of ways by examining the color and intensity characteristics of an image. After the analysis, a determination is received from the image analysis system of whether the image is a not found image or a true image.11-17-2011
20120195495Hierarchical Tree AAM - An active appearance model is built by arranging the training images in its training library into a hierarchical tree with the training images at each parent node being divided into two child nodes according to similarities in characteristic features. The number of node levels is such that the number of training images associated with each leaf node is smaller than a predefined maximum. A separate AAM, one per leaf node, is constructed using each leaf node's corresponding training images. In operation, starting at the root node, a test image is compared with each parent node's two child nodes and follows a node-path of model images that most closely matches the test image. The test image is submitted to an AAM selected for being associated with the leaf node at which the test image rests. The selected AAM's output aligned image may be resubmitted to the hierarchical tree if sufficient alignment is not achieved.08-02-2012
20120002868METHOD FOR FAST SCENE MATCHING - A method for identifying digital images having matching backgrounds from a collection of digital images, comprising using a processor to perform the steps of: determining a set of one or more feature values for each digital image in the collection of digital images, wherein the set of feature values includes an edge compactness feature value that is an indication of the number of objects in the digital image that are useful for scene matching; determining a subset of the collection of digital images that are good candidates for scene matching by applying a classifier responsive to the determined feature values; applying a scene matching algorithm to the subset of the collection of digital images to identify groups of digital images having matching backgrounds; and storing an indication of the identified groups of digital images having matching backgrounds in a processor-accessible memory.01-05-2012
20110299765ROBUST PATTERN RECOGNITION SYSTEM AND METHOD USING SOCRATIC AGENTS - A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module.12-08-2011
20110299764METHOD FOR AUTOMATED CATEGORIZATION OF HUMAN FACE IMAGES BASED ON FACIAL TRAITS - A method for automated categorization of human face images based on facial traits, said method comprising a facial trait extracting phase, comprising the steps of: providing a multitude of images comprising human faces, for each image sampling a multitude of points in said image to obtain point sample data, for each sampled point extracting visual features from said point sample data, for each image assigning said visual features to predefined codewords by applying a codebook transform, for each image extracting facial traits by applying a kernel-based learning method's prediction algorithm to said codewords to establish the probability that a facial trait from a predefined set of facial traits is present in said image, and extract said facial trait for said image if said probability is higher than a predefined threshold.12-08-2011
20110293173Object Detection Using Combinations of Relational Features in Images - A classifier for detecting objects in images is constructed from a set of training images. For each training image, features are extracted from a window in the training image, wherein the window contains the object, and then randomly sample coefficients c of the features. N-combinations for each possible set of the coefficients are determined. For each possible combination of the coefficients, a Boolean valued proposition is determined using relational operators to generate a propositional space. Complex hypotheses of a classifier are defined by applying combinatorial functions of the Boolean operators to the propositional space to construct all possible logical propositions in the propositional space. Then, the complex hypotheses of the classifier can be applied to features in a test image to detect whether the test image contains the object.12-01-2011
20090052768IDENTIFYING A SET OF IMAGE CHARACTERISTICS FOR ASSESSING SIMILARITY OF IMAGES - The invention relates to a method (02-26-2009
20100303343METHOD FOR FACE RECOGNITION AND SYNTHESIS - A method of recognizing an object in an image is provided, the method comprises the following steps. The image having the object is provided, and principal traits of the object are encoded in order to generate a first trait code. The first trait code is compared with data stored in a database so as to obtain a plurality of differences. A minimum of the plurality of differences is found. This method can be applied to synthesize human faces.12-02-2010
20090310854Multi-Label Multi-Instance Learning for Image Classification - Described is a technology by which an image is classified (e.g., grouped and/or labeled), based on multi-label multi-instance data learning-based classification according to semantic labels and regions. An image is processed in an integrated framework into multi-label multi-instance data, including region and image labels. The framework determines local association data based on each region of an image. Other multi-label multi-instance data is based on relationships between region labels of the image, relationships between image labels of the image, and relationships between the region and image labels. These data are combined to classify the image. Training is also described.12-17-2009
20090310855EVENT DETECTION METHOD AND VIDEO SURVEILLANCE SYSTEM USING SAID METHOD - An event detection method for video surveillance systems and a related video surveillance system are described. The method comprises a learning phase, wherein learning images of a supervised area are acquired at different time instants in the absence of any detectable events, and an operating detection phase wherein current images of said area are acquired. The method detects an event by comparing a current image with an image corresponding to a linear combination of a plurality of reference images approximating, or coinciding with, respective learning images.12-17-2009
20100080452COEFFICIENT LEARNING APPARATUS AND METHOD, IMAGE PROCESSING APPARATUS AND METHOD, PROGRAM, AND RECORDING MEDIUM - A coefficient learning apparatus includes a regression coefficient calculation unit configured to obtain a tap from an image of a first signal; a regression prediction value calculation unit configured to perform a regression prediction computation; a discrimination information assigning unit configured to assign discrimination information to the pixel of interest; a discrimination coefficient calculation unit configured to obtain a tap from the image of the first signal; a discrimination prediction value calculation unit configured to perform a discrimination prediction computation; and a classification unit configured to classify each of the pixels of the image of the first signal into one of the first discrimination class and the second discrimination class. The regression coefficient calculation unit further calculates the regression coefficient using only the pixels classified as the first discrimination class and further calculates the regression coefficient using only the pixel classified as the second discrimination class.04-01-2010
20090208096TRANSFORMING MEASUREMENT DATA FOR CLASSIFICATION LEARNING08-20-2009
20090087085TRACKER COMPONENT FOR BEHAVIORAL RECOGNITION SYSTEM - A tracker component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The behavior-recognition system may be configured to learn, identify, and recognize patterns of behavior by observing a video stream (i.e., a sequence of individual video frames). The tracker component may be configured to track objects depicted in the sequence of video frames and to generate, search, match, and update computational models of such objects.04-02-2009
20100080450CLASSIFICATION VIA SEMI-RIEMANNIAN SPACES - Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.04-01-2010
20120269425PREDICTING THE AESTHETIC VALUE OF AN IMAGE - A system and method for determining the aesthetic quality of an image are disclosed. The method includes extracting a set of local features from the image, such as gradient and/or color features and generating an image representation which describes the distribution of the local features. A classifier system is used for determining an aesthetic quality of the image based on the computed image representation.10-25-2012
20090263011Detection Technique for Digitally Altered Images - Techniques are generally described to determine whether a JPEG image has undergone two compressions. Probabilities can be computed for the first digits of quantized DCT (discrete cosine transform) coefficients from a set of AC (alternate current) modes to detect or determine whether the JPEG image has undergone two compressions. The set of AC modes may include a predetermined number of distinguishable AC modes where a distinguishable AC mode may be an AC mode in which a second quantization step (QS10-22-2009
20090263010ADAPTING A PARAMETERIZED CLASSIFIER TO AN ENVIRONMENT - A classifier is trained on a first set of examples, and the trained classifier is adapted to perform on a second set of examples. The classifier implements a parameterized labeling function. Initial training of the classifier optimizes the labeling function's parameters to minimize a cost function. The classifier and its parameters are provided to an environment in which it will operate, along with an approximation function that approximates the cost function using a compact representation of the first set of examples in place of the actual first set. A second set of examples is collected, and the parameters are modified to minimize a combined cost of labeling the first and second sets of examples. The part of the combined cost that represents the cost of the modified parameters applied to the first set is calculated using the approximation function.10-22-2009
20110170769CLASSIFIER LEARNING IMAGE PRODUCTION PROGRAM, METHOD, AND SYSTEM - A classifier learning image production program, method, and system are provided which are capable of efficiently acquiring learning images to be employed in development of a discrimination application, or more particularly, efficiently acquiring initial learning images to be employed in an early stage of development of a discrimination algorithm. A classifier learning image production program allows a computer to execute the steps of inputting an image; detecting a discrimination area from the inputted image, acquiring plural detected data, and recording the detected data in a storage device; integrating the plural detected data to obtain learning image candidate information, and recording the learning image candidate information as the detected data in the storage device; clipping plural learning images from the inputted images, and recording the plural learning images as learning image data in the storage device; classifying the learning images into one or more sets; and displaying the learning images on a display device.07-14-2011
20110170768Image segregation system with method for handling textures - In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, for example, a same texture, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.07-14-2011
20090290788System and method for performing multi-image training for pattern recognition and registration - A system and method for performing multi-image training for pattern recognition and registration is provided. A machine vision system first obtains N training images of the scene. Each of the N images is used as a baseline image and the N−1 images are registered to the baseline. Features that represent a set of corresponding image features are added to the model. The feature to be added to the model may comprise an average of the features from each of the images in which the feature appears. The process continues until every feature that meets a threshold requirement is accounted for. The model that results from the present invention represents those stable features that are found in at least the threshold number of the N training images. The model may then be used to train an alignment/inspection tool with the set of features.11-26-2009
20090110268TABLE OF CONTENTS EXTRACTION BASED ON TEXTUAL SIMILARITY AND FORMAL ASPECTS - An initial organizational table for a document is determined based on textual similarity between entries of the organizational table and target text fragments and not taking into account text formatting. A classifier is trained to identify text fragment pairs consisting of entries of the organizational table and corresponding target text fragments based at least in part on text formatting features. The training employs a training set of examples annotated based on the initial organizational table. The initial organizational table is updated using the trained classifier.04-30-2009
20100272351INFORMATION PROCESSING APPARATUS AND METHOD FOR DETECTING OBJECT IN IMAGE DATA - Learning is sequentially executed with respect to weak discriminators based on learning data held in a storage device. Upon learning, an evaluation value for the weak discriminator is calculated. It is discriminated, based on a shift of the evaluation value, whether or not the learning is overlearning. If it is discriminated that the learning is overlearning, new learning data is added. Thus, the overlearning is easily detected and the learning is efficiently executed.10-28-2010
20090285474System and Method for Bayesian Text Classification - A method for classifying text comprises receiving data containing text and parsing a plurality of tokens out of the text. A plurality of metatokens are generated for each token, wherein the metatokens comprise strings of text and groupings of strings of text. The method further comprises calculating a probability that the data falls into a certain category, using the tokens and metatokens. The probability is compared to a threshold value and the data is classified into the certain category if the probability is greater than the threshold value.11-19-2009
20110206275IMAGE ORIENTATION DETERMINATION DEVICE, IMAGE ORIENTATION DETERMINATION METHOD, AND IMAGE ORIENTATION DETERMINATION PROGRAM - When positive image similarity (degree of training image similarity between input image features and those of positive training image) is higher than a predetermined first threshold, image orientation determination decision section determines input image orientation. When negative image similarity (degree of training image similarity between input image features and those of a negative training image) is not lower than a predetermined second threshold value, the image orientation determination decision section does not determine input image orientation. When the image orientation determination decision section determines the orientation of the input image, image orientation determination section calculates orientation similarity reflecting similarity between input image features and those stored in orientation-specific features storage section. If the calculated orientation similarity satisfies a predetermined condition, the image orientation determination section determines input image orientation according to positive training image orientation related to the image features stored in the orientation-specific features storage section.08-25-2011
20080240551LOCAL BI-GRAM MODEL FOR OBJECT RECOGNITION - A local bi-gram model object recognition system and method for constructing a local bi-gram model and using the model to recognize objects in a query image. In a learning phase, the local bi-gram model is constructed that represents objects found in a set of training images. The local bi-gram model is a local spatial model that only models the relationship of neighboring features without any knowledge of their global context. Object recognition is performed by finding a set of matching primitives in the query image. A tree structure of matching primitives is generated and a search is performed to find a tree structure of matching primitives that obeys the local bi-gram model. The local bi-gram model can be found using unsupervised learning. The system and method also can be used to recognize objects unsupervised that are undergoing non-rigid transformations for both object instance recognition and category recognition.10-02-2008
20120294515IMAGE PROCESSING APPARATUS AND IMAGE PROCESSING METHOD, LEARNING APPARATUS AND LEARNING METHOD, PROGRAM, AND RECORDING MEDIUM - A predictive signal processing unit calculates a pixel value of a luminance component of a pixel of interest by a calculation of a predictive coefficient for a luminance component and a luminance prediction tap. A predictive signal processing unit calculates a pixel value of a chrominance component of a pixel of interest by a calculation of a predictive coefficient for a chrominance component which is higher in noise reduction effect than the predictive coefficient for the luminance component and a chrominance prediction tap. For example, the present technology can be applied to an image processing apparatus.11-22-2012
20120294514TECHNIQUES TO ENABLE AUTOMATED WORKFLOWS FOR THE CREATION OF USER-CUSTOMIZED PHOTOBOOKS - A system and method for generating a photobook are provided. The method includes receiving a set of images and automatically selecting a subset of the images as candidates for inclusion in a photobook. At least one design element of a design template for the photobook is automatically selected, based on information extracted from at least one of the images in the subset. Placeholders of the design template are automatically filled with images drawn from the subset to form at least one page of a multipage photobook. The exemplary system and method address some of the problems of photobook creation, thorough combining automatic methods for selecting, cropping, and placing photographs into a photo album template, which the user can then post-edit, if desired. This can greatly reduce the time required to create a photobook and thus encourage users to print photo albums.11-22-2012
20120141020IMAGE CLASSIFICATION - Images are classified as photos (e.g., natural photographs) or graphics (e.g., cartoons, synthetically generated images), such that when searched (online) with a filter, an image database returns images corresponding to the filter criteria (e.g., either photos or graphics will be returned). A set of image statistics pertaining to various visual cues (e.g., color, texture, shape) are identified in classifying the images. These image statistics, combined with pre-tagged image metadata defining an image as either a graphic or a photo, may be used to train a boosting decision tree. The trained boosting decision tree may be used to classify additional images as graphics or photos based on image statistics determined for the additional images.06-07-2012
20080292181Information Processing Method, Information Processing Apparatus, and Storage Medium Storing a Program - An information processing method includes: for image data of each of a plurality of images, obtaining scene information concerning the image data from supplemental data that is appended to the image data, classifying a scene of an image represented by the image data, based on the image data, comparing the classified scene with a scene indicated by the scene information; and if there is a mismatch image for which the classified scene does not match the scene indicated by the scene information, displaying information concerning the mismatch image on a confirmation screen.11-27-2008
20090232390Image processing device, image processing method, learning device, learning method, and program - An image processing device, to convert a first image data into a second image data having a higher image quality, includes: a predicted tap extracting unit to extract multiple pixels as a predicted tap for prediction computing; a level limit class tap extracting unit to extract multiple pixels as a level limit class tap for level limit classifying processing; a waveform class tap extracting unit to extract multiple pixels as a waveform class tap; a level limit classifying unit to classify the pixel of interest, based on a ratio between a level width and a dynamic range of the pixels making up the level limit class tap; a waveform pattern classifying unit to classify the pixel of interest; a prediction coefficient output unit to output a prediction coefficient corresponding to a combination of a level limit class and a waveform pattern class; and a prediction computing unit.09-17-2009
20120141019REGION DESCRIPTION AND MODELING FOR IMAGE SUBSCENE RECOGNITION - A method and apparatus is described here that categorizes images by extracting regions and describing the regions with a 16-dimensional subscene feature vector, which is a concatenation of color, texture, and spatial feature vectors. By comparing the spatial feature vectors in images with similarly-obtained feature vectors in a Gaussian mixture based model pool (obtained in a subscene modeling phase), the images may be categorized (in a subscene recognition phase) with probabilities relating to each region or subscene. Higher probabilities are likelier correlations. The device may be a single or multiple core CPU, or parallelized vector processor for characterizing many images. The images may be photographs, videos, or video stills, without restriction. When used real-time, the method may be used for visual searching or sorting.06-07-2012
20120141017REDUCING FALSE DETECTION RATE USING LOCAL PATTERN BASED POST-FILTER - A training set for a post-filter classifier is created from the output of a face detector. The face detector can be a Viola Jones face detector. Face detectors produce false positives and true positives. The regions in the training set are labeled so that false positives are labeled negative and true positives are labeled positive. The labeled training set is used to train a post-filter classifier. The post-filter classifier can be an SVM (Support Vector Machine). The trained face detection classifier is placed at the end of a face detection pipeline comprising a face detector, one or more feature extractors and the trained post-filter classifier. The post-filter reduces the number of false positives in the face detector output while keeping the number of true positives almost unchanged using features different from the Haar features used by the face detector.06-07-2012
20090087084METHOD AND APPARATUS FOR PATTERN RECOGNITION - Methods and apparatuses for pattern recognition involve quantum-mechanical calculations. Pattern recognition can be achieved by considering a quantum system and its Hamiltonian dynamics. The dynamics are calculated on the basis of an initial Hamiltonian indicating an initial quantum state and on the basis of a final Hamiltonian. The final Hamiltonian depends on an input pattern and reference patterns. Transformations according to the Hamiltonian dynamics for the quantum system are applied to generate a final quantum state of said quantum system. Depending on said final quantum state a similarity between said input pattern and said reference patterns is determined.04-02-2009
20080317335METHOD OF IDENTIFICATION ACCORDING TO SELECTED PATTERNS AND RELATED COMPUTER SYSTEM - A method of identification of a computer system includes: (a) selecting a first partial pattern from a pattern group; (b) determining whether the first partial pattern selected in step (a) corresponds with a predetermined rule; and (c) controlling the computer system whether to operate a program according to a determining result in step (b).12-25-2008
20090285473METHOD AND APPARATUS FOR OBTAINING AND PROCESSING IMAGE FEATURES - Machine-readable media, methods, apparatus and system for obtaining and processing image features are described. In some embodiments, groups of training features derived from regions of training images may be trained to obtain a plurality of classifiers, each classifier corresponding to each group of training features. The plurality of classifiers may be used to classify groups of validation features derived from regions of validation images to obtain a plurality of weights, wherein each weight corresponds to each region of the validation images and indicates how important the each region of the validation images is. Then, a weight may be discarded from the plurality of weights based upon a certain criterion.11-19-2009
20120070074Method and System for Training a Landmark Detector using Multiple Instance Learning - An apparatus and method for training a landmark detector receives training data which includes a plurality of positive training bags, each including a plurality of positively annotated instances, and a plurality of negative training bags, each including at least one negatively annotated instance. Classification function is initialized by training a first weak classifier based on the positive training bags and the negative training bags. All training instances are evaluated using the classification function. For each of a plurality of remaining classifiers, a cost value gradient is calculated based on spatial context information of each instance in each positive bag evaluated by the classification function. A gradient value associated with each of the remaining weak classifiers is calculated based on the cost value gradients, and a weak classifier is selected which has a lowest associated gradient value and given a weighting parameter and added to the classification function.03-22-2012
20120070073SEARCHING DOCUMENT IMAGES - Disclosed is a method of searching a digital image of a document for a predetermined keyword. The method identifies a word in the digital image, the word comprising one or more shapes. A test matrix comprising a difference vector for each character of the word is generated, and a template matrix comprising a difference vector for each shape of the keyword is also generated, wherein a difference vector represents the differences between the visual features of a respective shape and the visual features of a collection of reference shapes. A measure of similarity between the word and the keyword is generated by comparing the test matrix and the template matrix.03-22-2012
20090252405METHOD AND APPARATUS FOR DICTIONARY-BASED IMAGE PROCESSING - A method and system for processing a digitized image including multiple pixels is provided. Image processing involves determining a characteristic value for a set of image pixels, determining a classification for the set of pixels based on the characteristic value, accessing a dictionary including transforms, and retrieving transforms for the set of pixels based on said classification, and applying the transforms to the set of pixels to obtain output pixels.10-08-2009
20120063673METHOD AND APPARATUS TO GENERATE OBJECT DESCRIPTOR USING EXTENDED CURVATURE GABOR FILTER - A method and apparatus to generate an object descriptor using extended curvature gabor filters. The method and apparatus may increase a recognition rate of even a relatively small image with use of an extended number of curvature gabor filters having controllable curvatures and may reduce the amount of calculation required for face recognition by performing the face recognition using only some of the extended curvature gabor filters which have a great effect on the recognition rate. The object descriptor generating method includes extracting gabor features from an input object image by applying a plurality of curvature gabor filters, generated via combination of a plurality of curvatures and a plurality of Gaussian magnitudes, to the object image, and generating an object descriptor for object recognition by projecting the extracted features onto a predetermined base vector.03-15-2012
20110142330IMAGE PROCESSING APPARATUS AND METHOD - An image processing apparatus and an image processing method, the image processing apparatus including: an image input unit which receives an image; and an image processing unit which generates reference data on the basis of a plurality of learning images classified into a plurality of first classes according to a noise characteristic and a plurality of second classes according to an image characteristic, and which performs scaling for the received image on the basis of the generated reference data.06-16-2011
20130216127IMAGE SEGMENTATION USING REDUCED FOREGROUND TRAINING DATA - Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.08-22-2013
20110222760METHOD FOR IDENTIFYING MARKED IMAGES BASED AT LEAST IN PART ON FREQUENCY DOMAIN COEFFICIENT DIFFERENCES - Briefly, in accordance with one embodiment, a method of identifying marked images based at least in part on frequency domain coefficient differences is disclosed.09-15-2011
20090097741SMOTE ALGORITHM WITH LOCALLY LINEAR EMBEDDING - A data classification method. The method includes: providing data mapped in a first space; mapping the data into a second space using locally linear embedding to generate mapped data; applying a synthetic minority over-sampling technique (SMOTE) to the mapped data to generate new data; and mapping the new data into the first space.04-16-2009
20090097740Method and Apparatus for Automating an Inspection Procedure - A process for using a hand-held infrared inspection system incorporating on-board training, on-board validation, on-board operator certification, on-board reporting information, or on-board survey instructions. Improved methods for automating area surveys are provided through exception-driven surveillance practices. Imbedded information enables less experienced operators to use more sophisticated devices more effectively. Validation or certification assures operator knowledge or ability. Multilevel classification of anomalies aids in automated analysis and report generation.04-16-2009
20130121565Method and Apparatus for Local Region Selection - Methods and apparatus for local region selection are described. A scribble-based, edge-aware local region selection tool or module that implements a local region selection method may allow a user to draw scribbles or strokes indicating different classes of content. The method may train Gaussian mixture models (GMMs) for each class from the user input. The GMMs may be applied to the image to generate a probability map for each class. Post-processing may be optionally performed to remove structural outliers. The probability maps may be smoothed using a geodesic smoothing technique. A geodesic smoothing technique may be applied that considers other classes when smoothing each class to limit or prevent propagation of a region corresponding to the class into other regions corresponding to other classes. The smoothed probability maps may be combined to generate a final region selection mask.05-16-2013
20130121566Automatic Image Adjustment Parameter Correction - Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.05-16-2013
20090016599SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.01-15-2009
20110229020LEARNING METHOD AND APPARATUS FOR PATTERN RECOGNITION - A method for information processing includes a learning process to generate a tree structured dictionary based on a plurality of patterns including a target object to be recognized. The method includes selecting a plurality of points from an input pattern based on a distribution of a probability that the target object to be recognized is present in the input pattern at each node of a tree structure generated in the learning process, and classifying the input pattern into a branch based on a value of a predetermined function that corresponds to values of the input pattern at selected plurality of points.09-22-2011
20120141018L1-Optimized AAM Alignment - An Active Appearance Model, AAM, uses an L06-07-2012
20090316982TRANSFORMING MEASUREMENT DATA FOR CLASSIFICATION LEARNING12-24-2009
20090316983Real-Time Action Detection and Classification - The present invention relates to a method and system for creating a strong classifier based on motion patterns wherein the strong classifier may be used to determine an action being performed by a body in motion. When creating the strong classifier, action classification is performed by measuring similarities between features within motion patterns. Embodiments of the present invention may utilize candidate part-based action sets and training samples to train one or more weak classifiers that are then used to create a strong classifier.12-24-2009
20100150432METHOD AND MACHINE FOR DIGITALLY CATALOGUING ARTICLES - Method for digitally cataloguing articles, in which a revolving support supports an article and turns it around a vertical axis of rotation, a plurality of images of the article are acquired synchronously with its rotation y two digital cameras positioned at respective observation points on a fixed structure, the acquired images of the article are filed in an image database, the filed images are made-up together with information pertinent to the article extracted from a stock control program of the articles to catalogue, and the thus made-up pages are published on the Internet.06-17-2010
20090116735WARNING APPARATUS AND METHOD FOR AVOIDING EYE STRESS - An exemplary warning method for avoiding eye stress of a computer user includes: capturing a number of consecutive images of the face of a computer user; processing the images to obtain a number of values each indicative of a degree of openness of the eyes of the computer user; counting an amount of values exceeding a predetermined threshold to obtain accumulated viewing time of the computer user; and triggering a warning means if the accumulated viewing time of the computer satisfies a predetermined condition.05-07-2009
20090116736SYSTEMS AND METHODS TO AUTOMATICALLY CLASSIFY ELECTRONIC DOCUMENTS USING EXTRACTED IMAGE AND TEXT FEATURES AND USING A MACHINE LEARNING SUBSYSTEM - A document analysis system that automatically classifies documents by recognizing in each document distinctive features comprises a document acquisition system, a document recognition training system, a document classification system, a document recognition system, and a job organization system. The document acquisition system receives jobs wherein each job containing at least one electronic document. The document feature recognition system automatically extracts image and text features from each received document. The document classification system automatically classifies recognized electronic documents by finding the best match between the extracted features of each of the document and feature sets associated with each category of document. The document recognition training system automatically trains the feature set for each corresponding category of documents, wherein the training system using extracted features of unrecognized documents automatically modifies the feature set for a document category. The job organization system automatically organizes each job according to the document categories it contains.05-07-2009
20090116734IMAGE CLASSIFICATION - An apparatus and method are provided for classifying elements in an image, in particular elements of a hyperspectral image, where an element is defined by a vector of feature values. The apparatus includes a classifier arrangement comprising a number of classifiers each operable, in respect of an element to be classified, to receive a different predetermined subset of the feature values from the element feature vector and wherein, in operation, each classifier is trained in respect of a predetermined set of classes using training data representative of elements in each class; and a combining arrangement operable to combine outputs from the classifiers to determine which of the predetermined classes to associate with an element to be classified, wherein each of the different predetermined subsets of feature values comprise a different cyclic selection of the feature values such that, in operation, adjacent feature values in an element feature vector are input to different ones of the classifiers and all feature values are input to at least one classifier.05-07-2009
20100183218OBJECT DETERMINING DEVICE AND PROGRAM THEREOF - An object determining device includes imaging means for obtaining an image of the object, likelihood value calculating means for calculating a first likelihood value for the object shown in the image by use of the image obtained by the imaging means and a machine learning system and for calculating a second likelihood value for the object shown in the image by use of the image obtained by the imaging means and another machine learning system, the first likelihood value indicating a level of likelihood that the object is wearing the covering and the second likelihood value indicating a level of likelihood that the object is not wearing the covering, and determining means for determining whether or not the object, shown in the image obtained by the imaging means, is wearing a covering, on the basis of a ratio between the first likelihood value and the second likelihood value.07-22-2010
20090245625IMAGE TRIMMING DEVICE AND PROGRAM - An image trimming device involves: extracting a region of interest from an original image; detecting a set of features for each region of interest; determining whether each region of interest should be placed inside or outside a trimming frame based on the set of features and setting the trimming frame in the image; extracting an image inside the trimming frame; determining a positional relationship between each region of interest and the trimming frame and increasing or decreasing probability of each region of interest to be placed inside the trimming frame depending on if the region has a set of features similar to that of another region of interest previously placed inside the trimming frame or previously placed outside the trimming frame.10-01-2009
20100215257CAPTURING AND RECOGNIZING HAND POSTURES USING INNER DISTANCE SHAPE CONTEXTS - A system, method, and computer program product for recognizing hand postures are described. According to one aspect, a set of training images is provided with labels identifying hand states captured in the training images. Inner Distance Shape Context (IDSC) descriptors are determined for the hand regions in the training images, and fed into a Support Vector Machine (SVM) classifier to train it to classify hand shapes into posture classes. An IDSC descriptor is determined for a hand region in a testing image, and classified by the SVM classifier into one of the posture classes the SVM classifier was trained for. The hand posture captured in the testing image is recognized based on the classification.08-26-2010
20100177956SYSTEMS AND METHODS FOR SCALABLE MEDIA CATEGORIZATION - Systems and methods for automating digital file classification are described. The systems and methods include generating a plurality of classifiers from a plurality of first features of a plurality of first digital files, each of the plurality of first digital files having one or more associated annotations. A plurality of second features extracted from a plurality of second digital files is sorted according to the plurality of classifiers. A distance vector is determined between the second features and respective first features for the corresponding ones of the classifiers and the determined distances are ranked. A subset of matched files is selected based on the ranking. The subset of matched files correspond to respective one or more associated annotations. One or more annotations associated with the subset of matched files are associated to subsequently received digital files using the corresponding ones of the classifiers.07-15-2010
20100215255Iterative Data Reweighting for Balanced Model Learning - Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.08-26-2010
20100226564FRAMEWORK FOR IMAGE THUMBNAILING BASED ON VISUAL SIMILARITY - An apparatus and method for detecting a region of interest in an image are disclosed. Image representations for a set of images that have been manually annotated with regions of interest are stored, along with positive and negative representations of each image which are similarly derived to the image representations except that they are based on features extracted from patches within the region of interest and outside it, respectively. For an original image for which a region of interest is desired, the stored information for K similar images is automatically retrieved and used to train a classifier. The trained classifier provides, for each patch of the original image, a probability of being in a region of interest, based extracted features of the patch (represented, for example, as a Fisher vector), which can be used to determine a region of interest in the original image.09-09-2010
20110058734CLASSIFICATION OF IMAGES AS ADVERTISEMENT IMAGES OR NON-ADVERTISEMENT IMAGES - An advertisement image classification system trains a binary classifier to classify images as advertisement images or non-advertisement images and then uses the binary classifier to classify images of web pages as advertisement images or non-advertisement images. During a training phase, the classification system generates training data of feature vectors representing the images and labels indicating whether an image is an advertisement image or a non-advertisement Image. The classification system trains a binary classifier to classify Images using training data. During a classification phase, the classification system inputs a web page with an image and generates a feature vector for the image. The classification system then applies the trained binary classifier to the feature vector to generate a score indicating whether the image is an advertisement image or a non-advertisement image.03-10-2011
20100232686HIERARCHICAL DEFORMABLE MODEL FOR IMAGE SEGMENTATION - Described herein is a technology for facilitating deformable model-based segmentation of image data. In one implementation, the technology includes receiving training image data (09-16-2010
20100208983LEARNING DEVICE, LEARNING METHOD, IDENTIFICATION DEVICE, IDENTIFICATION METHOD, AND PROGRAM - A learning device includes a feature-point extracting section extracting feature points from a generation image, a feature-point feature-quantity extracting section extracting feature-point feature-quantities representing features of the feature points, a total-feature-quantity generating section generating a total feature quantity represented by a multi-dimensional vector, and an identifier generating section generating an identifier using the total feature quantity and a true label indicating whether or not the generation image is a positive image or a negative image.08-19-2010
20100080451IMAGE PROCESSING APPARATUS AND COEFFICIENT LEARNING APPARATUS - An image processing apparatus includes a storage unit in which regression coefficient data is stored for each class on the basis of a tap in which a linear feature amount corresponding to a pixel of interest of first image data and a non-linear feature amount determined from the image data are used as elements; a classification unit configured to classify each of linear feature amounts of a plurality of items of input data of the input first image into a predetermined class; a reading unit configured to read the regression coefficient data; and a data generation unit configured to generate data of a second image obtained by making the first image have higher quality by performing a product-sum computation process by using the regression coefficient data read from the reading unit and elements of the tap of each of the plurality of items of input data of the input first image.04-01-2010
20120033874Learning weights of fonts for typed samples in handwritten keyword spotting - A wordspotting system and method are disclosed. The method includes receiving a keyword and, for each of a set of typographical fonts, synthesizing a word image based on the keyword. A keyword model is trained based on the synthesized word images and the respective weights for each of the set of typographical fonts. Using the trained keyword model, handwritten word images of a collection of handwritten word images which match the keyword are identified. The weights allow a large set of fonts to be considered, with the weights indicating the relative relevance of each font for modeling a set of handwritten word images.02-09-2012
20090074288DATA PROCESSING APPARATUS, COMPUTER PROGRAM PRODUCT, AND DATA PROCESSING METHOD - A data processing apparatus includes a feature-value calculating unit that calculates an image feature value indicating a feature of image data, a case database including a case set including a correspondence of image feature values and functions, and an optimum-function predicting unit that predicts an optimum function based on the case database and the image feature value calculated by the feature-value calculating unit. Due to the optimum-function predicting unit, work efficiency of a user can be improved.03-19-2009
20090324060LEARNING APPARATUS FOR PATTERN DETECTOR, LEARNING METHOD AND COMPUTER-READABLE STORAGE MEDIUM - A learning apparatus for a pattern detector, which includes a plurality of weak classifiers and detects a specific pattern from input data by classifications of the plurality of weak classifiers, acquires a plurality of data for learning in each of which whether or not the specific pattern is included is given, makes the plurality of weak classifiers learn by making the plurality of weak classifiers detect the specific pattern from the acquired data for learning, selects a plurality of weak classifiers to be composited from the weak classifiers which have learned, and composites the plurality of weak classifiers into one composite weak classifier based on comparison between a performance of the composite weak classifier and performances of the plurality of weak classifiers.12-31-2009
20110026812OBJECT DETECTION APPARATUS - In order to improve object detection precision, an object detection apparatus includes a posterior probability calculation portion utilizing an occurrence probability of a background and a foreground acquired by utilizing a characteristic quantity extracted from each pixel of an input image and a probability density function, a posterior probability of the previous background and foreground, and a conditional probability indicating a relation of an event (background or foreground) to the vicinity of an attentive pixel in a spatial direction and a relation of an event to the vicinity of the attentive pixel in a temporal direction so as to calculate a posterior probability of the background and the foreground from a probability model utilizing a tendency that the same event appears together in the vicinity of the attentive pixel in the spatial and temporal directions; and an object determination portion for determining an object from comparison between the posterior probabilities of the background and the foreground.02-03-2011
20110176725LEARNING APPARATUS, LEARNING METHOD AND PROGRAM - A learning apparatus includes a learning section which learns, according as a learning image used for learning a discriminator for discriminating whether a predetermined discrimination target is present in an image is designated from a plurality of sample images by a user, the discriminator using a random feature amount including a dimension feature amount randomly selected from a plurality of dimension feature amounts included in an image feature amount indicating features of the learning image.07-21-2011
20100220922LEARNING APPARATUS AND OBJECT DETECTING APPARATUS - Feature values calculated from a peripheral image area of feature points extracted in a detection target object in a training image each are labeled with a label indicating a class of the detection target object, feature values calculated from a peripheral image area of feature points of a non detection target object in the training image each are labeled with a label indicating the non detection target object, voting positions in a parameter space are calculated by relative positions of the feature points of the detection target object from the detection target object on the training image, and a first classifier is learned using the labeled feature values extracted in the training image so that a class distribution is concentrated and the voting positions in the parameter space are concentrated.09-02-2010
20130129199OBJECT-CENTRIC SPATIAL POOLING FOR IMAGE CLASSIFICATION - A method is provided for classifying an image. The method includes inferring location information of an object of interest in an input representation of the image. The method further includes determining foreground object features and background object features from the input representation of the image. The method additionally includes pooling the foreground object features separately from the background object features using the location information to form a new representation of the image. The new representation is different than the input representation of the image. The method also includes classifying the image based on the new representation of the image.05-23-2013
20110150325Visual Object Appearance Modelling Using Image Processing - A computer-implemented method of generating a model from a set of images. The method comprises processing a plurality of data items, each data item representing an image of said set of images, to determine variability between said plurality of data items; and generating model data representing said model based upon said data items and said variability, wherein the influence of each of said data items upon the generated model is determined by a relationship between a respective one of said data items and said variability.06-23-2011
20100290700INFORMATION PROCESSING DEVICE AND METHOD, LEARNING DEVICE AND METHOD, PROGRAMS, AND INFORMATION PROCESSING SYSTEM - An information processing device including an extraction unit and a detection unit. If both a parameter set extracting features from an image and a classifier performing predetermined classification by using the extracted features are statistically learned in advance, the extraction unit extracts features of a recognition target object from an input image by using the parameter set, and the detection unit performs the predetermined classification by using the classifier, which uses the features extracted by the extraction unit, and, on the basis of the result of the classification, determines whether or not the object is included in the input image.11-18-2010
20130142419ENCODER OPTIMIZATION OF ADAPTIVE LOOP FILTERS IN HEVC - An optimized adaptive loop filter does not redesign filters inside the optimization loop of signaling depth which saves computations. Additionally, the Sum of Squared Errors (SSE) (distortion) of blocks is computed for the smallest blocks, thus, allowing for the distortion of larger blocks to be computed efficiently by adding block SSEs together which saves computations by removing redundant operations to calculate SSE of a block each time.06-06-2013
20130142421Method for Fast, Robust, Multi-Dimensional Pattern Recognition - A method and system for probe-based pattern matching including an apparatus for synthetic training of a model of a pattern. The apparatus comprises a sensor for obtaining an image of the pattern and a processor for receiving the image of the pattern from the sensor and running a program. In the steps performed by the program a boundary of the pattern in the image is identified. A plurality of positive probes are placed at selected points along the boundary of the pattern and at least one straight segment of the boundary of the pattern is identified. The at least one straight segment of the boundary is extended to provide an imaginary straight segment and a plurality of negative probes are placed at selected points along the imaginary straight segment, where each negative probe has a negative weight.06-06-2013
20110243426METHOD, APPARATUS, AND PROGRAM FOR GENERATING CLASSIFIERS - Classifiers, which are combinations of a plurality of weak classifiers, for discriminating objects included in detection target images by employing features extracted from the detection target images to perform multi class discrimination including a plurality of classes regarding the objects are generated. When the classifiers are generated, learning is performed for the weak classifiers of the plurality of classes, sharing only the features.10-06-2011
20110019907METHOD FOR IDENTIFYING MARKED IMAGES USING STATISTICAL MOMENTS BASED AT LEAST IN PART ON A JPEG ARRAY - Briefly, embodiments of a method of identifying marked images, in which higher order statistical moments based at least in part on a JPEG array are employed, is described01-27-2011
20110044534HIERARCHICAL CLASSIFIER FOR DATA CLASSIFICATION - Described herein is a framework for constructing a hierarchical classifier for facilitating classification of digitized data. In one implementation, a divergence measure of a node of the hierarchical classifier is determined. Data at the node is divided into at least two child nodes based on a splitting criterion to form at least a portion of the hierarchical classifier. The splitting criterion is selected based on the divergence measure. If the divergence measure is less than a predetermined threshold value, the splitting criterion comprises a divergence-based splitting criterion which maximizes subsequent divergence after a split. Otherwise, the splitting criterion comprises an information-based splitting criterion which seeks to minimize subsequent misclassification error after the split.02-24-2011
20110123101INDOOR-OUTDOOR DETECTOR FOR DIGITAL CAMERAS - An indoor-outdoor detection method includes constructing a first indoor-outdoor detector; and constructing a second indoor-outdoor detector. A normalized brightness of a subject image is determined and a comparison result is generated based on the determined normalized brightness and a threshold brightness value. One of the first detector or the second detector is selectively applied to the subject image based on the comparison result, and a detection result is generated. Image signal processing is performed on the subject image based on the detection result.05-26-2011
20120243779RECOGNITION DEVICE, RECOGNITION METHOD, AND COMPUTER PROGRAM PRODUCT - According to an embodiment, a recognition device includes a generation unit to select, plural times, groups each including learning samples from a storage unit, learn a classification metric for classifying the groups selected in each selection, and generate an evaluation metric including the classification metrics; a transformation unit to transform a first feature value of an image including an object into a second feature value using the evaluation metric; a calculation unit to calculate similarities of the object to categories in a table using the second feature value and reference feature values; and a registration unit to register the second feature value as the reference feature value in the table associated with the category of the object and register the first feature value as the learning sample belonging to the category of the object in the storage unit. The generation unit performs the generation again.09-27-2012
20110129145DETECTING FACIAL SIMILARITY BASED ON HUMAN PERCEPTION OF FACIAL SIMILARITY - Similar faces may be determined within images based on human perception of facial similarity. The user may provide an image including a query face to which the user wishes to find faces that are similar. Similar faces may be determined based on similarity information. Similarity information may be generated from information related to a human perception of facial similarity. Images that include faces determined to be similar, based on the similarity information, may be provided to the user as search result images. The user then may provide feedback to indicate the user's perception of similarity between the query face and the search result images.06-02-2011
20090041340Image Processing System, Learning Device and Method, and Program - The present invention relates to an image processing system, a learning device and method, and a program which enable easy extraction of feature amounts to be used in a recognition process. Feature points are extracted from a learning-use model image, feature amounts are extracted based on the feature points, and the feature amounts are registered in a learning-use model dictionary registration section 02-12-2009
20100014751IMAGE PROCESSING DEVICE, STORAGE MEDIUM, AND IMAGE PROCESSING METHOD - A face candidate of an object having movable ears is detected by a face candidate detection unit from an image of the object, and an ear of the object is detected by an attached site detection unit. The object is then detected from the image by a head portion determination unit in accordance with the detected face candidate and ear.01-21-2010
20110081073Methods And Logic For Autonomous Generation Of Ensemble Classifiers, And Systems Incorporating Ensemble Classifiers - In one embodiment, a method for generating an ensemble classifier may include transforming multidimensional training data into a plurality of response planes. Each of the response planes includes a set of confidence scores. The response planes are transformed into a plurality of binary response planes. Each of the binary response planes include a set of binary scores corresponding to one of the confidence scores. Combinations of the binary response planes are transformed into sets of diversity metrics according to a diversity measure. A metric is selected from the sets of diversity metrics. A predicted performance of a child combination of the recognition algorithms corresponding to the combinations is generated. The predicted performance is based at least in part upon the metrics. Parent recognition algorithms are selected from the recognition algorithms based at least in part upon the predicted performance. The ensemble classifier is generated and includes the parent recognition algorithms.04-07-2011
20100128975METHOD AND SYSTEM FOR OBJECT RECOGNITION BASED ON A TRAINABLE DYNAMIC SYSTEM - A system for object recognition in which a multi-dimensional scanner generates a temporal sequence of multi-dimensional output data of a scanned object. That data is then coupled as an input signal to a trainable dynamic system. The system exemplified by a general-purpose recurrent neural network is previously trained to generate an output signal representative of the class of the object in response to a temporal sequence of multi-dimensional data.05-27-2010
20110075917ESTIMATING AESTHETIC QUALITY OF DIGITAL IMAGES - A method for estimating the aesthetic quality of an input digital image comprising using a digital image processor for performing the following: determining one or more vanishing point(s) associated with the input digital image by automatically analyzing the digital image; computing a compositional model from at least the positions of the vanishing point(s); and producing an aesthetic quality parameter for the input digital image responsive to the compositional model, wherein the aesthetic quality parameter is an estimate for the aesthetic quality of the input digital image.03-31-2011
20110075918Method and system for learning object recognition in images - In a first exemplary embodiment of the present invention, an automated, computerized method for learning object recognition in an image is provided. According to a feature of the present invention, the method comprises the steps of providing a training set of standard images, calculating intrinsic images corresponding to the standard images and building a classifier as a function of the intrinsic images.03-31-2011
20110150324METHOD AND APPARATUS FOR RECOGNIZING AND LOCALIZING LANDMARKS FROM AN IMAGE ONTO A MAP - Method and apparatus for recognizing landmark buildings in an image and then locating the recognized landmark buildings onto a map together with related information wherein a first database is employed to store models formed by mathematical set descriptions of landmark buildings which are learned from a set of training images of a model-learning module captured by an imaging device for each building, and a second database is employed to store the related information of each landmark building. The model of each landmark building is represented as a set of features and the geometric relationship between them by clustering the salient features extracted from a set of training images of the landmark building.06-23-2011
20110150323CATEGORIZATION QUALITY THROUGH THE COMBINATION OF MULTIPLE CATEGORIZERS - A system categorizes one or more objects based at least in part upon one or more characteristics associated therewith. A first classifier includes a rule set to determine if each of the one or more objects meets or exceeds a quality threshold. A second classifier, orthogonal to the first classifier, includes a rule set to determine if each of the one or more objects meets or exceeds a quality threshold. In one embodiment, the quality threshold associated with the first classifier and the quality threshold associated with the second classifier are less than a predetermined target threshold. The result for each object of the first classifier is compared to the result of the second classifier. The object is categorized if the result of the first classifier and the result of the second classifier match. The object is uncategorized if the result of the first classifier does not match the result of the second classifier.06-23-2011
20100310158Method And Apparatus For Training Classifier, Method And Apparatus For Image Recognition - Embodiments of the present invention provide a method and apparatus for training an image classifier. The method includes: A. dividing a set of training images for classifier training into a positive-example sample set and at least two negative-example sample sets; B. determining, for each negative-example sample set, a feature set for differentiating the positive-example sample set from the negative-example sample set; and C. performing training using each feature set determined to obtain a classifier. This invention also provides a method and apparatus for image recognition utilizing the image classifier.12-09-2010
20130163859REGRESSION TREE FIELDS - A new tractable model solves labeling problems using regression tree fields, which represent non-parametric Gaussian conditional random fields. Regression tree fields are parameterized by non-parametric regression trees, allowing universal specification of interactions between image observations and variables. The new model uses regression trees corresponding to various factors to map dataset content (e.g., image content) to a set of parameters used to define the potential functions in the model. Some factors define relationships among multiple variable nodes. Further, the training of regression trees is scalable, both in the training set size and in the fact that the training can be parallelized. In one implementation, maximum pseudolikelihood learning provides for joint training of various aspects of the model, including feature test selection and ordering (i.e., the structure of the regression trees), parameters of each factor in the graph, and the scope of the interacting variable nodes used in the graph.06-27-2013
20100310156Image Data Processing And Arrangements Therefor - Image data processing is facilitated. According to an example embodiment, image data is processed using photometric similarity and, where appropriate, a classification of sample pixels for the image data. In some applications, a trained bilateral filter function is used with a filter coefficient selected for a particular classification of image data to filter artifacts in the image data.12-09-2010
20080247639Data Matching Method, Data Matching Apparatus and Data Matching Program - The purpose is to execute the matching of a data after state change from a data before state change, or the matching of a data before state change from a data after state change. The component analyzing unit realized by software analyzes an input data by using a configuration component of a selected state-specific database and sends the configuration component coefficient to the component coefficient conversion unit. The component coefficient conversion unit converts the sent configuration component coefficient into the configuration component coefficient corresponding to the state-specific database which is not selected and sends it to the state change data generation unit. The state change data generation unit generates the state change data by using the sent configuration component coefficient and the configuration component of the corresponding state-specific database. The matching unit matches the state change data and the registration data accumulated in the matching data accumulating unit.10-09-2008
20100296728Discrimination Apparatus, Method of Discrimination, and Computer Program - A discrimination apparatus includes: a feature-quantity extraction section extracting a feature quantity from an object of discrimination; and a discriminator including a plurality of weak discriminators expressed as a Bayesian network having each node to which a corresponding one of two or more of the feature quantities input from the feature-quantity extraction section is allocated and a combiner combining individual discrimination results of the object of discrimination by the plurality of weak discriminators.11-25-2010
20080253645Adaptive Classifier, and Method of Creation of Classification Parameters Therefor - A method of generating classifier parameters from a plurality of multivariate sample data, for use in subsequent classification, said classifier parameters relating to a plurality of intervals on each of the variables, said intervals being associated with classes, comprising: inputting said sample data; calculating a plurality of boundaries for each of said variables from said sample data, and deriving parameters defining said intervals from said boundaries.10-16-2008
20080205750Method for Adaptively Boosting Classifiers for Object Tracking - A method adapts a boosted classifier to new samples. A boosted classifier is trained using initial samples. The boosted classifier is a combination of weak classifiers. Each weak classifier of the boosted classifier is updated adaptively by adding contributions of new samples and deleting contributions old samples.08-28-2008
20110164812METHOD, APPARATUS AND SYSTEM FOR ORIENTING A DISORIENTED IMAGE - A method, apparatus and system for orienting a disoriented image, and a method, apparatus and system for training a plurality of Gaussian mixture models (GMMs) to orient the disoriented image are provided. The method of training the plurality of GMMs includes: obtaining a plurality of color and texture features from the disoriented image; selecting a plurality of discriminative features from the color and texture features; calculating probabilities of each of the GMMs orienting the disoriented image, where each of the GMMs represents one of a plurality of rotation classes, and each of the rotation classes represents a rotation angle that is a multiple of a right angle. Furthermore, the system includes an electronic device that includes an embedded platform including a processor which processes the disoriented image.07-07-2011
20100246940METHOD OF GENERATING HDR IMAGE AND ELECTRONIC DEVICE USING THE SAME - A method of generating a high dynamic range image and an electronic device using the same are described. The method includes loading a brightness adjustment model created by a neural network algorithm; obtaining an original image; acquiring a pixel characteristic value, a first characteristic value in a first direction, and a second characteristic value in a second direction of the original image; and generating an HDR image through the brightness adjustment model according to the pixel characteristic value, the first characteristic value, and the second characteristic value of the original image. The electronic device includes a brightness adjustment model, a characteristic value acquisition unit, and a brightness adjustment procedure. The electronic device acquires a pixel characteristic value, a first characteristic value, and a second characteristic value of an original image through the characteristic value acquisition unit, and generates an HDR image from the original image through the brightness adjustment model.09-30-2010
20100329544INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing apparatus includes the following elements. A learning unit is configured to perform Adaptive Boosting Error Correcting Output Coding learning using image feature values of a plurality of sample images each being assigned a class label to generate a multi-class classifier configured to output a multi-dimensional score vector corresponding to an input image. A registration unit is configured to input a register image to the multi-class classifier, and to register a multi-dimensional score vector corresponding to the input register image in association with identification information about the register image. A determination unit is configured to input an identification image to be identified to the multi-class classifier, and to determine a similarity between a multi-dimensional score vector corresponding to the input identification image and the registered multi-dimensional score vector corresponding to the register image.12-30-2010
20100310157Apparatus and method for video sensor-based human activity and facial expression modeling and recognition - An apparatus and method for human activity and facial expression modeling and recognition are based on feature extraction techniques from time sequential images. The human activity modeling includes determining principal components of depth and/or binary shape images of human activities extracted from video clips. Independent Component Analysis (ICA) representations are determined based on the principal components. Features are determined through Linear Discriminant Analysis (LDA) based on the ICA representations. A codebook is determined using vector quantization. Observation symbol sequences in the video clips are determined. And human activities are learned using the Hidden Markov Model (HMM) based on status transition and an observation matrix.12-09-2010
20120148148METHOD FOR DETECTING FIRE-FLAME USING FUZZY FINITE AUTOMATA - A method for detecting a fire flame using fuzzy finite automata is provided. The fire-flame detection method comprises (1) acquiring an image required for the detection of fire-flame, (2) dividing the image into a number of blocks, (3) extracting a fire-flame candidate block using a brightness distortion of a pixel in the block, (4) detecting a fire-flame candidate region from the fire-flame block using a color probability model, and (5) determining whether the fire-flame candidate region corresponds to a fire-flame via fuzzy finite automata. The fire-flame detection method can detect fire-flames in a variety of fire images with relatively high precision, by establishing a probability model using the brightness distortion and wavelet energy in fire-flame regions with continuous and irregular fluctuation patterns and using the upward motion, and applying the model to fuzzy finite automata.06-14-2012
20110135191APPARATUS AND METHOD FOR RECOGNIZING IMAGE BASED ON POSITION INFORMATION - According to the present invention, the amount of computation required for image recognition processing can be reduced by extracting only image recognition learning information for an object that may appear in a region having the geographical property of a current position and comparing the image recognition learning information with ambient-image information.06-09-2011
20100215254Self-Learning Object Detection and Classification Systems and Methods - A method of object classification based upon fusion of a remote sensing system and a natural imaging system is provided. The method includes detecting an object using the remote sensing system. An angle of view of a video camera of the natural imaging system is varied. An image including the object is generated using the natural imaging system. The natural imaging system may zoom in on the object. The image represented in either pixel or transformed space is compared to a plurality of templates via a competition based neural network learning algorithm. Each template has an associated label determined statistically. The template with a closest match to the image is determined. The image may be assigned the label associated with the relative location of the object, the relative speed of the object, and the label of the template determined statistically to be the closest match to the image.08-26-2010
20100215256METHOD AND DEVICE FOR MAINTAINING IMAGE BACKGROUND BY MULTIPLE GAUSSIAN MODELS - A method maintaining an image background by multiple Gaussian models utilized to a device includes the following steps. First, the device captures an image frame having pixels to obtain background information, and then calculates the background information to establish a primary Gaussian model. Next, the device captures continuous image frames in a time period to obtain and calculate graphic information for establishing a secondary Gaussian model, and then repeates the steps to establish multiple secondary Gaussian models. Finally, the device compares two secondary Gaussian models, and then updates learning for the primary Gaussian model by the secondary Gaussian model if the graphic information of the secondary Gaussian models are attributable to the background information, or maintains the background information of the primary Gaussian model without updating the learning if anyone of the graphic information of the two secondary Gaussian models is unattributable to the background information.08-26-2010
20110052046SYSTEM AND METHOD FOR VISUAL SEARCHING OF OBJECTS USING LINES - Disclosed is method of visual search for objects that include straight lines. A two-step process is used, which includes detecting straight line segments in an image. The lines are generally characterized by their length, midpoint location, and orientation. Hypotheses that a particular straight line segment belongs to a known object are generated and tested. The set of hypotheses is constrained by spatial relationships in the known objects. The speed and robustness of the method and apparatus disclosed makes it immediately applicable to many computer vision applications.03-03-2011
20100158356SYSTEM AND METHOD FOR IMPROVED CLASSIFICATION - A system and method for improved classification. A first classifier is trained using a first process running on at least one computing device using a first set of training images relating to a class of images. A set of additional images are selected using the first classifier from a source of additional images accessible to the computing device. The first set of training images and the set of additional images are merged using the computing device to create a second set of training images. A second classifier is trained using a second process running on the computing device using the second set of training images. A set of unclassified images are classified using the second classifier thereby creating a set of classified images. The first classifier and the second classifier employ different classification methods.06-24-2010
20090175533INFORMATION PROCESSING APPARATUS AND METHOD, RECORDING MEDIUM, AND PROGRAM - In an information processing apparatus, such as a robot that discriminates human faces, nodes are hierarchically arranged in a tree structure. Each of the nodes has a number of weak classifiers. Each terminal node learns face images associated with one label. An upper node learns learning samples of all labels learned by lower nodes. When a window image to be classified is input, discrimination is performed sequentially from upper nodes to lower nodes. When it is determined that the window image does not correspond to a human face, discrimination by lower nodes is not performed, and discrimination proceeds to sibling nodes.07-09-2009
20090175532Method and System for Creating Flexible Structure Descriptions - In one embodiment, the invention provides a method, comprising detecting data fields on a scanned image; generating a flexible document description based on the detected data fields, including creating a set of search elements for each data field, each search element having associated search criteria; and training the flexible document description using a search algorithm to detect the data fields on additional training images based on the set of search elements.07-09-2009
20090175531SYSTEM AND METHOD FOR FALSE POSITIVE REDUCTION IN COMPUTER-AIDED DETECTION (CAD) USING A SUPPORT VECTOR MACNINE (SVM) - A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-processing machine learning to maximize specificity and sensitivity of the classification to realize a reduction in number of false positive detections reported. The method includes training a classifier on a set of medical image training data selected to include a number of true and false regions, wherein the true and false regions are identified by a CAD process, and automatically segmented, wherein the segmented training regions are reviewed by at least one specialist to classify each training region for its ground truth, i.e., true or false, essentially qualifying the automatic segmentation, wherein a feature pool is identified and extracted from each segmented region, and wherein the pool of features is processed by genetic algorithm to identify an optimal feature subset, which subset is used to train a support vector machine, detecting, within non-training medical image data, regions that are candidates for classification, segmenting the candidate regions, extracting a set of features from each segmented candidate regions and classifying the candidate region using the support vector machine after training in accordance with the optimal feature subset, and processing the set of candidate features.07-09-2009
20110188743IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, IMAGE PROCESSING SYSTEM, AND RECORDING MEDIUM - An image processing apparatus applying image processing corresponding to a predetermined imaging region of a subject of an image obtained by capturing the imaging region to the image includes an acquisition unit configured to acquire classification information obtained by classifying a plurality of imaging regions according to characteristics of image processing corresponding to each of the imaging regions, an identification unit configured to identify, after identifying which of the groups the imaging region of the image corresponds to, which of the plurality of the imaging regions included in the corresponding group the imaging region of the image corresponds to, based on the image and the classification information, and a processing unit configured to apply the image processing corresponding to the identified imaging region to the image.08-04-2011
20110188742RECOMMENDING USER IMAGE TO SOCIAL NETWORK GROUPS - A method of recommending social group(s) for sharing one or more user images, includes using a processor for acquiring the one or more user images and their associated metadata; acquiring one or more group images from the social group(s) and their associated metadata; computing visual features for the user images and the group images; and recommending social group(s) for the one of more user images using both the visual features and the metadata.08-04-2011
20100027875AUTOMATED LEARNING FOR PEOPLE COUNTING SYSTEMS - A system, method and program product for providing automated learning for a people counting system. A system is disclosed that includes a grid system for dividing a field of view (FOV) of a captured image data into a set of blocks; an object detection and tracking system for tracking a blob passing through the FOV; and a learning system that maintains person size parameters for each block and updates person size parameters for a selected block when a blob appears in the selected block.02-04-2010
20110216965Image Segmentation Using Reduced Foreground Training Data - Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.09-08-2011
20110216964META-CLASSIFIER SYSTEM FOR VIDEO ANALYTICS - A system for meta-classification having a training phase mechanism and an operational phase mechanism. The training phase mechanism may have a detection and tracking module, a classifier section connected to the detection and tracking module, a feature synthesis module connected to the classifier section, a labeling module connected to the feature synthesis module and a training data module connected to the labeling module. The operational phase mechanism may have a detection and tracking module, a classifier section connected to the detection and tracking module, a feature synthesis module connected to the classifier section and a meta-classification module connected to the feature synthesis module and the training module. The training phase mechanism may provide parameters and settings to the operational phase mechanism.09-08-2011
20120308124Method and System For Localizing Parts of an Object in an Image For Computer Vision Applications - A method is provided for localizing parts of an object in an image by training local detectors using labeled image exemplars with fiducial points corresponding to parts within the image. Each local detector generates a detector score corresponding to the likelihood that a desired part is located at a given location within the image exemplar. A non-parametric global model of the locations of the fiducial points is generated for each of at least a portion of the image exemplars. An input image is analyzed using the trained local detectors, and a Bayesian objective function is derived for the input image from the non-parametric model and detector scores. The Bayesian objective function is optimized using a consensus of global models, and an output is generated with locations of the fiducial points labeled within the object in the image.12-06-2012
20120308123APPARATUS AND METHOD FOR ESTIMATING THE NUMBER OF OBJECTS INCLUDED IN AN IMAGE - An apparatus and method for estimating the number of objects in an input image are disclosed. The apparatus includes: a learning unit that calculates counted values of a linear regression function by learning an arbitrary image; a separation unit that separates a foreground region and a background region of the input image; an extraction unit that searches for features that require an amount of calculation that is below a particular threshold from features having high correlation with each other feature and extracts the features from the separated foreground region; and an estimation unit that estimates the number of objects in the foreground region as a dependent variable by allocating the counted values of the linear regression function that are calculated by the learning unit and the features that are extracted by the extraction unit as independent variables of a linear regression function.12-06-2012
20090097739PEOPLE DETECTION IN VIDEO AND IMAGE DATA - A process identifies a person in image data. The process first executes a training phase, and thereafter a detection phase. The training phase learns body parts using body part detectors, generates classifiers, and determines a spatial distribution and a set of probabilities. The execution phase applies the body part detector to an image, combines output of several body part detectors, and determines maxima of the combination of the output.04-16-2009
20110305384INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing apparatus includes a first generation unit that generates learning images corresponding to a learning moving image, a first synthesis unit that generates a synthesized learning image such that a plurality of the learning images is arranged at a predetermined location and synthesized, a learning unit that computes a feature amount of the generated synthesized learning image, and performs statistical learning using the feature amount to generate a classifier, a second generation unit that generates determination images, a second synthesis unit that generates a synthesized determination image such that a plurality of the determination images is arranged at a predetermined location and synthesized, a feature amount computation unit that computes a feature amount of the generated synthesized determination image, and a determination unit that determines whether or not the determination image corresponds to a predetermined movement.12-15-2011
20090154796SYSTEMS AND METHODS FOR HUMAN BODY POSE ESTIMATION - Systems and computer-implemented methods for use in body pose estimation are provided. Training data is obtained, where the training data includes observation vector data and corresponding pose vector data for a plurality of images. The observation vector data is representative of the images in observation space. The pose vector data is representative of the same images in pose space. Based on the training data, a model is computed that includes parameters of mapping from the observation space to latent space, parameters of mapping from the latent space to the pose space, and parameters of the latent space. The latent space has a lower dimensionality than the observation space and the pose space.06-18-2009
20110075919Techniques for Enabling or Establishing the Use of Face Recognition Algorithms - Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.03-31-2011
20120039527COMPUTER-READABLE MEDIUM STORING LEARNING-MODEL GENERATING PROGRAM, COMPUTER-READABLE MEDIUM STORING IMAGE-IDENTIFICATION-INFORMATION ADDING PROGRAM, LEARNING-MODEL GENERATING APPARATUS, IMAGE-IDENTIFICATION-INFORMATION ADDING APPARATUS, AND IMAGE-IDENTIFICATION-INFORMATION ADDING METHOD - A computer-readable medium storing a learning-model generating program causing a computer to execute a process is provided. The process includes: extracting feature values from an image for learning that is an image whose identification information items are already known, the identification information items representing the content of the image; generating learning models by using binary classifiers, the learning models being models for classifying the feature values and associating the identification information items and the feature values with each other; and optimizing the learning models for each of the identification information items by using a formula to obtain conditional probabilities, the formula being approximated with a sigmoid function, and optimizing parameters of the sigmoid function so that the estimation accuracy of the identification information items is increased.02-16-2012
20120099783GENERATION AND USAGE OF ATTRACTIVENESS SCORES - A digital image is obtained. A face depicted in the digital image is detected. A set of characteristics is obtained, where the set of characteristics are associated with at least some portion of a face. An attractiveness score is generated based at least in part on the detected face and the set of characteristics.04-26-2012
20110026811IMAGE PROCESSING APPARATUS AND METHOD, DATA PROCESSING APPARATUS AND METHOD, AND PROGRAM AND RECORDING MEDIUM - The image processing apparatus and method, and the program and the recording medium according to the present invention can make the coefficient vector into high precision by noise elimination or correction utilizing the mutual correlation of the divided image areas in the intermediate eigenspace, and allows relaxation of the input condition and robustness. The high correlation in the divided image areas in the intermediate eigenspace can reduce the divided image areas to be processed, and actualize reduction in processing load and enhancement of the processing speed.02-03-2011
20110091098System and Method for Detecting Text in Real-World Color Images - A method and apparatus for detecting text in real-world images comprises calculating a cascade of classifiers, the cascade comprising a plurality of stages, each stage including one or more weak classifiers, the plurality of stages organized to start out with classifiers that are most useful for ruling out non-text regions, and removing regions classified as non-text regions from the cascade prior to completion of the cascade, to further speed up processing.04-21-2011
20110091097APPARATUS OF LEARNING RECOGNITION DICTIONARY, AND METHOD OF LEARNING RECOGNITION DICTIONARY - There are provided a characteristic obtaining unit configured to obtain a subject characteristic including a characteristic of a subject, an image processing unit configured to generate a duplicate subject image by performing an image process to an image of the subject according to the subject characteristic obtained by the characteristic obtaining unit, and a learning unit configured to learn a matching dictionary by using the duplicate subject image generated by the image processing unit. Thus, it is possible to reduce the number of subject images necessary for the learning.04-21-2011
20110064301TEXTUAL ATTRIBUTE-BASED IMAGE CATEGORIZATION AND SEARCH - Techniques and systems for providing textual attribute-based image categorization and search are disclosed herein. In some aspects, images may be analyzed to identify a category of an image, or portion thereof. Additional textual attributes may be identified and associated with the image. In various aspects, the categories may be types of sky sceneries. Categorized images may be searched based on the categories and/or attributes. In further aspects, a user interface may provide an intuitive arrangement of the images for user navigation and selection. The user interface may also provide a simplified presentation and search of the categorized images. Images selected from user interface may be used to replace or modify features of an existing target image.03-17-2011
20110064303Object Recognition Using Textons and Shape Filters - Given an image of structured and/or unstructured objects, semantically meaningful areas are automatically partitioned from the image, each area labeled with a specific object class. Shape filters are used to enable capturing of some or all of the shape, texture, and/or appearance context information. A shape filter comprises one or more regions of arbitrary shape, size, and/or position within a bounding area of an image, paired with a specified texton. A texton comprises information describing the texture of a patch of surface of an object. In a training process a sub-set of possible shape filters is selected and incorporated into a conditional random field model of object classes. The conditional random field model is then used for object detection and recognition.03-17-2011
20110064302RECOGNITION VIA HIGH-DIMENSIONAL DATA CLASSIFICATION - A method is disclosed for recognition of high-dimensional data in the presence of occlusion, including: receiving a target data that includes an occlusion and is of an unknown class, wherein the target data includes a known object; sampling a plurality of training data files comprising a plurality of distinct classes of the same object as that of the target data; and identifying the class of the target data through linear superposition of the sampled training data files using l03-17-2011
20120002869System and method for detection of multi-view/multi-pose objects - The present invention provides a computer implemented process for detecting multi-view multi-pose objects. The process comprises training of a classifier for each intra-class exemplar, training of a strong classifier and combining the individual exemplar-based classifiers with a single objective function. This function is optimized using the two nested AdaBoost loops. The first loop is the outer loop that selects discriminative candidate exemplars. The second loop, the inner loop selects the discriminative candidate features on the selected exemplars to compute all weak classifiers for a specific position such as a view/pose. Then all the computed weak classifiers are automatically combined into a final classifier (strong classifier) which is the object to be detected.01-05-2012
20120045120INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD - An information processing apparatus that discriminates the orientation of a target includes a calculation unit that calculates a distribution of a difference in feature amount between a plurality of learning patterns each showing the orientation of a target, a determination unit that determines, using a probability distribution obtained from the distribution of differences calculated by the calculation unit, a pixel that is in an input pattern and is to be referred to in order to discriminate the orientation of a target in the input pattern, and a discrimination unit that performs discrimination for obtaining the orientation of the target in the input pattern by comparing a feature amount of the pixel determined by the determination unit and a threshold set in advance.02-23-2012
20090220146METHOD AND APPARATUS FOR CHARACTERIZING THE FORMATION OF PAPER - A method for characterizing the formation of paper in which patterns and/or structures existing in the paper are automatically characterized and classified. The automatic characterization and classification includes creating a collection of paper specimens, creating a digital image of each individual specimen, digital pre-processing of the digital image where necessary, calculating different multi-dimensional features in light of the digital images or sub-ranges of the images, analyzing structure-specific groups forming in the feature space during calculation of the different multi-dimensional features and analyzing the structure-specific groups in the feature space, projecting the results of the analysis of the structure-specific groups into a—compared to the feature space—low-dimensional space for visualizing the analysis results, and drawing on the analysis results for the classification of newly added specimens. The calculation of the different multi-dimensional features takes place in light of the digital images or sub-ranges of the images on the basis of at least one of the following algorithms: relational kernel function (RKF), phase-based method, 2-point or 3-point method, or wavelets.09-03-2009
20120301015IMAGE IDENTIFICATION DEVICE, IMAGE IDENTIFICATION METHOD AND RECORDING MEDIUM - The invention provides an image identification device uses a separating plane to classify block images into the categories. The image identification device includes a target image input unit inputting a target image, a block image generation unit generates block images, a feature quantity computing unit computes feature quantities of the block images, and a category determination unit determines whether the block images are classified into the categories or not. The feature quantity computing unit uses local feature quantities of the block images and a global feature quantity of the target image as a whole, and also in a feature quantity space using features of the block images as coordinate axes, uses coordinate positions of feature quantity vectors optional areas in the feature quantity space to count the block images and causes the global feature quantity to include the number of the block images thus counted.11-29-2012
20120301014LEARNING TO RANK LOCAL INTEREST POINTS - Tools and techniques for learning to rank local interest points from images using a data-driven scale-invariant feature transform (SIFT) approach termed “Rank-SIFT” are described herein. Rank-SIFT provides a flexible framework to select stable local interest points using supervised learning. A Rank-SIFT application detects interest points, learns differential features, and implements ranking model training in the Gaussian scale space (GSS). In various implementations a stability score is calculated for ranking the local interest points by extracting features from the GSS and characterizing the local interest points based on the features being extracted from the GSS across images containing the same visual objects.11-29-2012
20120250982IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, PROGRAM, AND RECORDING MEDIUM - An image processing apparatus includes: an image feature outputting unit that outputs each of image features in correspondence with a time of the frame; a foreground estimating unit that estimates a foreground image at a time s by executing a view transform as a geometric transform on a foreground view model and outputs an estimated foreground view; a background estimating unit that estimates a background image at the time s by executing a view transform as a geometric transform on a background view model and outputs an estimated background view; a synthesized view generating unit that generates a synthesized view by synthesizing the estimated foreground and background views; a foreground learning unit that learns the foreground view model based on an evaluation value; and a background learning unit that learns the background view model based on the evaluation value by updating the parameter of the foreground view model.10-04-2012
20120155751OBJECT RECOGNITION APPARATUS, OBJECT RECOGNITION METHOD, LEARNING APPARATUS, LEARNING METHOD, STORAGE MEDIUM AND INFORMATION PROCESSING SYSTEM - A learning method of detectors used to detect a target object, comprises: a selection step of selecting a plurality of specific regions from a given three-dimensional model of the target object; a learning step of learning detectors used to detect the specific regions selected in the selection step; an evaluation step of executing recognition processing of positions and orientations of predetermined regions of the plurality of specific regions by the detectors learned in the learning step; and a normalization step of setting vote weights for outputs of the detectors according to recognition accuracies of results of the recognition processing in the evaluation step.06-21-2012
20120250983OBJECT DETECTING APPARATUS AND METHOD - An object detecting apparatus and method is disclosed. An object detecting apparatus comprises: a detection classifier, configured to detect an object in an input image to obtain one or more candidate objects; a verifying classifier, configured to verify each candidate object by using verifying features from an image block corresponding to the each candidate object; and an on-line learning device, configured to train and optimize the detection classifier by using image blocks corresponding to the candidate objects as on-line samples, based on verifying results of the candidate objects obtained by the verifying classifier.10-04-2012
20110103682MULTI-MODALITY CLASSIFICATION FOR ONE-CLASS CLASSIFICATION IN SOCIAL NETWORKS - A classification apparatus, method, and computer program product for multi-modality classification are disclosed. For each of a plurality of modalities, the method includes extracting features from objects in a set of objects. The objects include electronic mail messages. A representation of each object for that modality is generated, based on its extracted features. At least one of the plurality of modalities is a social network modality in which social network features are extracted from a social network implicit in the set of electronic mail messages. A classifier system is trained based on class labels of a subset of the set of objects and on the representations generated for each of the modalities. With the trained classifier system, labels are predicted for unlabeled objects in the set of objects.05-05-2011
20100092075Method of directed pattern enhancement for flexible recognition - A directed pattern enhancement method receives a learning image and pattern enhancement directive. Pattern enhancement learning is performed using the learning image and the pattern enhancement directive to generate pattern enhancement recipe. An application image is received and a pattern enhancement application is performed using the application image and the pattern enhancement recipe to generate pattern enhanced image. A recognition thresholding is performed using the pattern enhanced image to generate recognition result. The pattern enhancement directive consists of background directive, patterns to enhance directive, and patterns to suppress directive. An update learning method performs pattern enhancement progressive update learning.04-15-2010
20100092073SYSTEM AND METHOD FOR OBJECT RECOGNITION AND CLASSIFICATION USING A THREE-DIMENSIONAL SYSTEM WITH ADAPTIVE FEATURE DETECTORS - A method including imaging an object in three-dimensions; binning data of the imaged object into three-dimensional regions having a predetermined size; determining a density value p of the data in each bin; inputting the p density values of the bins into a first layer of a computational system including a corresponding processing element for each of the bins; calculating an output O of the processing elements of the computational system while restricting the processing elements to have weights Wc04-15-2010
20100092074FOREGROUND ACTION ESTIMATING APPARATUS AND FOREGROUND ACTION ESTIMATING METHOD - The present invention provides a foreground action estimating apparatus and a foreground action estimating method, wherein the foreground action estimating apparatus includes: a training image inputting means for inputting a foreground image, a background image and an image having the foreground and background images as training images; a basis matrix calculating means for calculating a foreground basis matrix and a background basis matrix by respectively extracting a foreground feature and a background feature from the foreground image and the background image, respectively, and combining the foreground basis matrix and the background basis matrix to obtain a combined basis matrix; a feature suppressing means for calculating the feature coefficients of the training images in accordance with the combined basis matrix obtained by the basis matrix calculating means so as to obtain image features of the background-feature-suppressed training images; and a foreground action information acquiring means for estimating foreground action information in accordance with a feature mapping matrix from the image feature to an action information set, by using the background-feature-suppressed image features.04-15-2010
20120121170OBJECT DETECTION SYSTEM BASED ON A POOL OF ADAPTIVE FEATURES - A method, system and computer program product for detecting presence of an object in an image are disclosed. According to an embodiment, a method for detecting a presence of an object in an image comprises: receiving multiple training image samples; determining a set of adaptive features for each training image sample, the set of adaptive features matching the local structure of each training image sample; integrating the sets of adaptive features of the multiple training image samples to generate an adaptive feature pool; determining a general feature based on the adaptive feature pool; and examining the image using a classifier determined based on the general feature to detect the presence of the object.05-17-2012
20120163706SHAPE DESCRIPTION AND MODELING FOR IMAGE SUBSCENE RECOGNITION - A method and apparatus is described here that categorizes images by extracting a subscene and describing the subscene with a top level feature vector and a division feature vector, which are descriptions of edge gradient classifications within rectangular bounding boxes. By filtering subscene feature vectors in images with a Gaussian mixture based model pool (obtained in a subscene modeling phase), the images may be categorized (in an subscene recognition phase) with probabilities relating to each subscene. Higher probabilities are likelier correlations. The device may be a single or multiple core CPU, or parallelized vector processor for characterizing many images. The images may be photographs, videos, or video stills, without restriction. When used real-time, the method may be used for visual searching or sorting.06-28-2012
20120213428TRAINING DEVICE, TRAINING SYSTEM AND METHOD - A training device comprises a first regenerating unit regenerates at least one of an image and a voice for training during the training courses which lead the user to train the operation of an input device, an operation accepting unit accepts the user operation for at least one of the image and the voice for training from a simulated user interface which simulates a user interface of the input device during training, a second regenerating unit regenerates at least one of the image and the voice for training when the training is ended, and a normal operation instructing unit instructs a normal operation to the user by outputting at least one of the image and the voice of the normal operation of the user, which show at least one of the image and the voice for training, which is synchronous with the regeneration of the second regenerating unit.08-23-2012
20120213427IMAGE PROCESSING APPARATUS AND IMAGE PROCESSING METHOD - Disclosed are an image processing apparatus and an image processing method. The image processing apparatus comprises a matching degree calculation unit configured to calculate respective matching degrees between an image waiting for processing and plural training images whose Kansei scores are pre-designated; and a Kansei score calculation unit configured to extract, from the plural training images, a predetermined number of training images corresponding to the maximum matching degree, and then based on the Kansei scores of the extracted training images with regard to a selected Kansei type, calculate a Kansei score of the image waiting for processing.08-23-2012
20120213426Method for Implementing a High-Level Image Representation for Image Analysis - Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high-level visual tasks, such low-level image representations are potentially not enough. The present invention provides a high-level image representation where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on this representation, superior performances on high-level visual recognition tasks are achieved with relatively classifiers such as logistic regression and linear SVM classifiers.08-23-2012
20120128238IMAGE PROCESSING DEVICE AND METHOD, DATA PROCESSING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM - An eigenprojection matrix (#05-24-2012
20120128237SUPERPIXEL-BOOSTED TOP-DOWN IMAGE RECOGNITION METHODS AND SYSTEMS - Systems and methods for implementing a superpixel boosted top-down image recognition framework are provided. The framework utilizes superpixels comprising contiguous pixel regions sharing similar characteristics. Feature extraction methods described herein provide non-redundant image feature vectors for classification model building. The provided framework differentiates a digitized image into a plurality of superpixels. The digitized image is characterized through image feature extraction methods based on the plurality of superpixels. Image classification models are generated from the extracted image features and ground truth labels and may then be used to classify other digitized images.05-24-2012
20120163708APPARATUS FOR AND METHOD OF GENERATING CLASSIFIER FOR DETECTING SPECIFIC OBJECT IN IMAGE - There provides an apparatus for and a method of generating a classifier for detecting a specific object in an image. The apparatus for generating a classifier for detecting a specific object in an image includes: a region dividing section for dividing, from a sample image, at least one square region having a side length equal to or shorter than the length of shorter side of the sample image; a feature extracting section for extracting an image feature from at least a part of the square regions divided by the region dividing section; and a training section for performing training based on the extracted image feature to generate a classifier. By using the apparatus for and method of generating the classifier, it becomes possible to make full use of recognizable regions of objects to be recognized with variable aspect ratios and improve speed and accuracy for recognizing in complex backgrounds.06-28-2012
20110182501METHOD FOR RECOGNIZING SHAPES AND SYSTEM IMPLEMENTING SAID METHOD - The invention includes a method for recognizing shapes using a preprocessing mechanism that decomposes a source signal into basic components called atoms and a recognition mechanism that is based on the result of the decomposition performed by the preprocessing mechanism. In the method, the preprocessing mechanism includes at least one learning phase culminating in a set of signals called kernels, the kernels being adapted to minimize a cost function representing the capacity of the kernels to correctly reconstruct the signals from the database while guaranteeing a sparse decomposition of the source signal while using a database of signals representative of the source to be processed and a coding phase for decomposing the source signal into atoms, the atoms being generated by shifting of the kernels according to their index, each of the atoms being associated with a decomposition coefficient. The invention also includes a shape recognition system for implementing the method.07-28-2011
20110182500CONTEXTUALIZATION OF MACHINE INDETERMINABLE INFORMATION BASED ON MACHINE DETERMINABLE INFORMATION - A system for contextualizing machine indeterminable information based on machine determinable information may include a memory, an interface, and a processor. The memory may store an electronic document image which may include information determinable by a machine and information indeterminable by a machine The processor may be operative to receive, via the interface, the electronic document image. The processor may determine the machine determinable information of the electronic document image and may identify the machine indeterminable information of the electronic document image. The processor may contextualize the machine indeterminable information based on the machine determinable information. The processor may present the contextualized machine indeterminable information to the user to facilitate interpretation thereof. In response thereto, the processor may receive, via the interface, data representative of a user determination associated with the machine indeterminable information.07-28-2011
20120314940IMAGE RECOGNITION DEVICE AND METHOD OF RECOGNIZING IMAGE THEREOF - An image recognition device in accordance with the inventive concept may include an input vector extraction part extracting an input vector from an input image; a compression vector conversion part converting the input vector into a compression vector using a projection vector; a training parameter generation part receiving a training vector to generate a training parameter using a projection vector obtained through a folding operation of the training vector; and an image classification part classifying the compression vector using the training vector to output image recognition data.12-13-2012
20120314939RECOGNIZING APPARATUS AND METHOD, PROGRAM, AND RECORDING MEDIUM - A predetermined feature point obtained from an input image is extracted. An image that indicates a locus specifying a predetermined graphic included in the input image and corresponds to a feature point is acquired using a Hough transform. A recognition target object is detected from an input image, based on a plurality of feature quantities, using an identifier generated by statistical learning using the plurality of feature quantities obtained from a locus image obtained based on a learning image including the recognition target object and a locus image obtained based on a learning image including no recognition target object.12-13-2012
20120314938Image Type Classifier For Improved Remote Presentation Session Compression - An invention is disclosed for classifying a graphic—e.g. as text or non-text. In embodiments, machine learning is used to generate a solution for classifying graphics of a graphic based on providing the machine learning system a plurality of graphics that are already classified. The way to determine a classification is then used by a remote presentation session server to classify tiles of frames to be transmitted to a client in a remote presentation session. The server encodes the tiles based on their classifications and transmits the encoded tiles to the client.12-13-2012
20120134579IMAGE PROCESSING DEVICE AND METHOD, DATA PROCESSING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM - In an image processing device and method, program, and recording medium of the present invention, high frequency components of a low quality image and a high quality image included in a studying image set are extracted, and an eigenprojection matrix and a projection core tensor of the high frequency components are generated in a studying step. In a restoration step, a first sub-core tensor and a second sub-core tensor are generated based on the eigenprojection matrix and the projection core tensor of the high frequency components, and a tensor projection process is applied to the high frequency components of an input image to generate a high quality image of the high frequency components. The high quality image of the high frequency components is added to an enlarged image obtained by enlarging the input image to the same size as an output image.05-31-2012
20120134578SYSTEM AND METHOD FOR DETECTING GLOBAL HARMFUL VIDEO - A system for detecting a global harmful video includes: a video determination policy generation unit for determining harmfulness of learning video segments from video learning information to analyze occurrence information of harmful learning video segments, and generating a global harmfulness determination policy based on the occurrence information; and a video determination policy execution unit for determining harmfulness of input video segments from information of an input video to analyze occurrence information of harmful input video segments, and determining whether the input video is harmful or not based on the occurrence information of the harmful input video segments and the global harmfulness determination policy.05-31-2012
20120134577INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing apparatus includes: a distinguishing unit which, by using an ensemble classifier, which includes a plurality of weak classifiers outputting weak hypotheses which indicates whether a predetermined subject is shown in an image in response to inputs of a plurality of features extracted from the image, and a plurality of features extracted from an input image, sequentially integrates the weak hypotheses output by the weak classifiers in regard to the plurality of features and distinguishes whether the predetermined subject is shown in the input image based on the integrated value. The weak classifier classifies each of the plurality of features to one of three or more sub-divisions based on threshold values, calculates sum divisions of the sub-divisions of the plurality of features as whole divisions into which the plurality of features is classified, and outputs, as the weak hypothesis, a reliability degree of the whole divisions.05-31-2012
20120163707MATCHING TEXT TO IMAGES - Text in web pages or other text documents may be classified based on the images or other objects within the webpage. A system for identifying and classifying text related to an object may identify one or more web pages containing the image or similar images, determine topics from the text of the document, and develop a set of training phrases for a classifier. The classifier may be trained and then used to analyze the text in the documents. The training set may include both positive examples and negative examples of text taken from the set of documents. A positive example may include captions or other elements directly associated with the object, while negative examples may include text taken from the documents, but from a large distance from the object. In some cases, the system may iterate on the classification process to refine the results.06-28-2012
20120170835Determining the Uniqueness of a Model for Machine Vision - Described are methods and apparatuses, including computer program products, for determining model uniqueness with a quality metric of a model of an object in a machine vision application. Determining uniqueness involves receiving a training image and a first set of model parameters, generating a first model of an object, generating a second model of the object based on the training image and a second set of model parameters modified from the first set of model parameters, determining a set of poses that represent possible instances of the second model in the training image, and computing a quality metric of the first model based on an evaluation of the set of poses with respect to the training image.07-05-2012
20120076401IMAGE CLASSIFICATION EMPLOYING IMAGE VECTORS COMPRESSED USING VECTOR QUANTIZATION - Local descriptors are extracted from an image. An image vector is generated having vector elements indicative of parameters of mixture model components of a mixture model representing the extracted local descriptors. The image vector is compressed using a vector quantization algorithm to generate a compressed image vector. Optionally, the compressing comprises splitting the image vector into a plurality of sub-vectors each including at least two vector elements, compressing each sub-vector independently using the vector quantization algorithm, and concatenating the compressed sub-vectors to generate the compressed image vector. Optionally, each sub-vector includes only vector elements indicative of parameters of a single mixture model component, and any sparse sub-vector whose vector elements are indicative of parameters of a mixture model component that does not represent any of the extracted local descriptors is not compressed.03-29-2012
20100272349REAL-TIME ANNOTATION OF IMAGES IN A HUMAN ASSISTIVE ENVIRONMENT - A method, information processing system, and computer program storage product annotate video images associated with an environmental situation based on detected actions of a human interacting with the environmental situation. A set of real-time video images are received that are captured by at least one video camera associated with an environment presenting one or more environmental situations to a human. One or more user actions made by the human that is associated with the set of real-time video images with respect to the environmental situation are monitored. A determination is made, based on the monitoring, that the human driver has one of performed and failed to perform at least one action associated with one or more images of the set of real-time video images. The one or more images of the set of real-time video images are annotated with a set of annotations.10-28-2010
20100272350METHODS AND APPARATUS TO PERFORM IMAGE CLASSIFICATION BASED ON PSEUDORANDOM FEATURES - Example methods and apparatus to perform image classification based on pseudorandom features are disclosed. A disclosed example method includes generating first and second pseudorandom numbers, extracting a first feature of an image based on the first and second pseudorandom numbers, and determining a classification for the image based on the first extracted feature.10-28-2010
20120257820IMAGE ANALYSIS TOOLS - A master image can be generated based upon evaluation of virtual machine images. The master image includes single instances of data segments that are shared across virtual machine images within a virtual machine environment. The master image can be further be constructed as a function of a peer pressure technique that includes data segments common to a majority of the virtual machine images within the master image. The data segments included within the master image can further be defined by prioritizing data within virtual machine images as well as identifying influential data with a peer pressure technique.10-11-2012
20120224765TEXT REGION DETECTION SYSTEM AND METHOD - A method for detecting a text region in an image is disclosed. The method includes detecting a candidate text region from an input image. A set of oriented gradient images is generated from the candidate text region, and one or more detection window images of the candidate text region are captured. A sum of oriented gradients is then calculated for a region in one of the oriented gradient images. It is classified whether each detection window image contains text by comparing the associated sum of oriented gradients and a threshold. Based on the classifications of the detection window images, it is determined whether the candidate text region is a true text region.09-06-2012
20120257819Vision-Based Object Detection by Part-Based Feature Synthesis - A method is provided for training and using an object classifier to identify a class object from a captured image. A plurality of still images is obtained from training data and a feature generation technique is applied to the plurality of still images for identifying candidate features from each respective image. A subset of features is selected from the candidate features using a similarity comparison technique. Identifying candidate features and selecting a subset of features is iteratively repeated a predetermined number of times for generating a trained object classifier. An image is captured from an image capture device. Features are classified in the captured image using the trained object classifier. A determination is made whether the image contains a class object based on the trained object classifier associating an identified feature in the image with the class object.10-11-2012
20120189193DETECTION OF OBJECTS REPRESENTED IN IMAGES - The invention relates to computer vision, in particular detection and classification of objects captured in a video stream of images. The invention provides a memory efficient method of storing images that have been pre-processed for use in object detection. The method is based on using histograms of orientation. The invention also includes methods for training and using weak classifiers that use this pre-processing of images. A first weak classifier uses the total count of two orientation values in a histogram as an index to a two dimensional confidence table to determine a confidence value. The second weak classifier projects one or more total counts of orientation values in a histogram into a scalar value that is then used in a one dimensional confidence map to determine a confidence value. Aspects of the invention include methods, computer systems and software.07-26-2012
20120189192Imaging Method and Apparatus with Optimized Grayscale Value Window Determination - A self-learning imaging method is particularly suited radiation imaging, such as for mammography. A plurality of training data sets are displayed on a display apparatus. A grayscale value setting is selected for each training data set. A feature set with at least one feature is assigned to each training data set. The feature set and the grayscale value setting are stored for each training data set. The grayscale value setting of an examination data set is selected according to the feature sets and the grayscale value setting of the training data sets.07-26-2012
20120082372AUTOMATIC DOCUMENT IMAGE EXTRACTION AND COMPARISON - Systems and methods are described that extract and match images from a first document with images in other documents. A user controls a threshold on the level of image noise to be ignored and a page range for faster processing of large documents.04-05-2012
20120082371LABEL EMBEDDING TREES FOR MULTI-CLASS TASKS - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for label embedding trees for large multi-class tasks. In one aspect, a method includes mapping each image in a plurality of images and each label in a plurality of labels into a multi-dimensional label embedding space. A tree of label predictors is trained with the plurality of mapped images such that an error function is minimized in which the error function counts an error for each mapped image if any of the label predictors at any depth of the tree incorrectly predicts that the mapped image belongs to the label predictor's respective label set.04-05-2012
20090016600COGNITIVE MODEL FOR A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM - A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.01-15-2009
20110123102IMAGE PROCESSING DEVICE, METHOD THEREOF, AND STORAGE MEDIUM STORING IMAGE PROCESSING PROGRAM - An image processing device includes a dictionary data storage unit to store dictionary data regarding features that a plurality of objects has, an arithmetic unit to compute feature data of an input image based on information of the input image that includes an object with a specific feature among the plurality of objects, and a calculation unit to calculate a parameter for adjusting the dictionary data regarding the object with the specific feature based on the feature data and the dictionary data.05-26-2011
20100254595GRAPHIC RECOGNITION DEVICE, GRAPHIC RECOGNITION METHOD, AND GRAPHIC RECOGNITION PROGRAM - A graphic recognition device, method, and recognition program recognize graphics without being influenced by an image shadow area. Image input unit acquires the image of the outside environment of a vehicle using a vehicle mounted camera. A light source location information acquiring unit calculates location of a light source such as the sun using the acquired image. User vehicle shape acquiring unit and other vehicle shape acquiring unit generate shape information for the vehicles indicating the location of points forming vehicle contours. Shadow area calculating unit calculates, on the basis of both vehicles' shape information, the object shape information and the light source location information, the location coordinates of the shadow area, and converts the location coordinates into two-dimensional coordinates to the shadow/non-shadow area emphasis flag recognizing unit, which recognizes the flag in the image by judging the presence/absence of the recognition object in each shadow and non-shadow area specified.10-07-2010
20100254596METHOD AND SYSTEM FOR GENERATING AN ENTIRELY WELL-FOCUSED IMAGE OF A LARGE THREE-DIMENSIONAL SCENE - A method and system for generating an entirely well-focused image of a three-dimensional scene. The method comprises the steps of a) learning a prediction model including at least a focal depth probability density function (PDF), h(k), for all depth values k, from historical tiles of the scene; b) predicting the possible focal surfaces in subsequent tiles of the scene by applying the prediction model; c) for each value of k, examining h(k) such that if h(k) is below a first threshold, no image is acquired at the depth k′ for said one tile; and if h(k) is above or equal to a first threshold, one or more images are acquired in a depth range around said value of k for said one tile; and d) processing the acquired images to generate a pixel focus map for said one tile.10-07-2010
20120237117OPTIMAL GRADIENT PURSUIT FOR IMAGE ALIGNMENT - A method for image alignment is disclosed. In one embodiment, the method includes acquiring a facial image of a person and using a discriminative face alignment model to fit a generic facial mesh to the facial image to facilitate locating of facial features. The discriminative face alignment model may include a generative shape model component and a discriminative appearance model component. Further, the discriminative appearance model component may have been trained to estimate a score function that minimizes the angle between a gradient direction and a vector pointing toward a ground-truth shape parameter. Additional methods, systems, and articles of manufacture are also disclosed.09-20-2012
20120237116Identifying Text Pixels in Scanned Images - A processor and method make use of multiple weak classifiers to construct a single strong classifier to identify regions that contain text within an input image document. The weak classifiers are grouped by their computing cost from low to median to high, and each weak classifier is assigned a weight value based on its ability to accurately identify text regions. A level 1 classifier is constructed by selecting weak classifiers from the low group, a level 2 classifier is constructed by selecting weak classifiers from the low and median groups, and a level 3 classifier is constructed by selecting weak classifiers from the low, median and high groups. Regions that the level 1 classifier identifies as containing text are submitted to the level 2 classifier, and regions that the level 2 classifier identifies as containing text are submitted to the level 3 classifier.09-20-2012
20120093398SYSTEM AND METHOD FOR MULTI-AGENT EVENT DETECTION AND RECOGNITION - A method and system for creating a histogram of oriented occurrences (HO2) is disclosed. A plurality of entities in at least one image are detected and tracked. One of the plurality of entities is designated as a reference entity. A local 2-dimensional ground plane coordinate system centered on and oriented with respect to the reference entity is defined. The 2-dimensional ground plane is partitioned into a plurality of non-overlapping bins, the bins forming a histogram, a bin tracking a number of occurrences of an entity class. An occurrence of at least one other entity of the plurality of entities located in the at least one image may be associated with one of the plurality of non-overlapping bins. A number of occurrences of entities of at least one entity class in at least one bin may be into a vector to define an HO2 feature.04-19-2012
20120093397Method and System for Learning Based Object Detection in Medical Images - Methods and Systems for training a learning based classifier and object detection in medical images is disclosed. In order to train a learning based classifier, positive training samples and negative training samples are generated based on annotated training images. Features for the positive training samples and the negative training samples are extracted. The features include an extended Haar feature set including tip features and corner features. A discriminative classifier is trained based on the extracted features.04-19-2012
20120093396DIGITAL IMAGE ANALYSIS UTILIZING MULTIPLE HUMAN LABELS - Systems and methods for implementing a multi-label image recognition framework for classifying digital images are provided. The provided multi-label image recognition framework utilizes an iterative, multiple analysis path approach to model training and image classification tasks. A first iteration of the multi-label image recognition framework generates confidence maps for each label, which are shared by the multiple analysis paths to update the confidence maps in subsequent iterations. The provided multi-label image recognition framework permits model training and image classification tasks to be performed more accurately than conventional single-label image recognition frameworks.04-19-2012
20120269426FEATURE SELECTION METHOD AND APPARATUS, AND PATTERN DISCRIMINATION METHOD AND APPARATUS - A feature selection apparatus, which selects features to be used to discriminate an object by a discriminator using learning data including the object, extracts a plurality of partial data from the learning data, and obtains discrimination values obtained by controlling the discriminator to process the plurality of extracted partial data as features of the plurality of partial data. The feature selection apparatus evaluates the obtained features based on discrimination degrees on a discrimination space defined by the discriminator, and selects features to be used to discriminate the object from a plurality of features obtained in association with the plurality of partial data based on an evaluation result.10-25-2012
20110229018CENTRALIZED INFORMATION PROCESSING APPARATUS AND CENTRALIZED INFORMATION PROCESSING SYSTEM - According to one embodiment, a centralized information processing apparatus includes an information acquisition unit configured to acquire image data, sorting destinations arranged in a descending order of scores obtained by character recognition and score information items thereof, and sorting information, a recognition-rate processing unit configured to provide information related to a recognition rate for each sorting destination, a changed parameter value acquisition unit configured to acquire a new parameter value, a simulation executing unit configured to execute a simulation of a character recognition process for the image data by using the changed parameter value, a difference list providing unit configured to form and provide a difference list indicating different content between new sorting information obtained as the simulation result and original sorting information, and a parameter changing unit configured to change the parameter value to the new parameter value.09-22-2011
20110229017ANNOTATION ADDITION METHOD, ANNOTATION ADDITION SYSTEM USING THE SAME, AND MACHINE-READABLE MEDIUM - Disclosed are a method and a system for adding annotations into an input medium file. The method comprises a step of creating annotation detection models based on training samples formed by existing media files having annotations; a step of extracting coexistence coefficients of any two annotations based on appearance frequencies of the annotations in the training samples; a step of inputting the input medium file; a step of extracting sense-of-vision features from the input medium file; a step of obtaining initial annotations of the input medium file; a step of acquiring candidate annotations based on the initial annotations and the coexistence coefficients of the annotations in the training samples; and a step of selecting a final annotation set from the candidate annotations based on the sense-of-vision features of the input medium file and the coexistence coefficients by using the annotation detection models.09-22-2011
20120321174Image Processing Using Random Forest Classifiers - A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.12-20-2012
20100232685IMAGE PROCESSING APPARATUS AND METHOD, LEARNING APPARATUS AND METHOD, AND PROGRAM - An image processing apparatus includes: an edge intensity detecting unit configured to detect the edge intensity of an image in increments of blocks having a predetermined size; a parameter setting unit configured to set an edge reference value used for extraction of an edge point that is a pixel used for detection of the blurred degree of the image based on a dynamic range that is difference between the maximum value and the minimum value of the edge intensities; and an edge point extracting unit configured to extract a pixel as the edge point with the edge intensity being equal to or greater than the edge reference value, and also the pixel value of a pixel within a block being included in an edge block that is a block within a predetermined range.09-16-2010
20120321175LOCATION-AIDED RECOGNITION - A mobile device having the capability of performing real-time location recognition with assistance from a server is provided. The approximate geophysical location of the mobile device is uploaded to the server. Based on the mobile device's approximate geophysical location, the server responds by sending the mobile device a message comprising a classifier and a set of feature descriptors. This can occur before an image is captured for visual querying. The classifier and feature descriptors are computed during an offline training stage using techniques to minimize computation at query time. The classifier and feature descriptors are used to perform visual recognition in real-time by performing the classification on the mobile device itself.12-20-2012
20120321176METHODS AND APPARATUSES FOR FACILITATING OBJECT RECOGNITION - Methods and apparatuses are provided for facilitating object recognition. A method may include accessing data for a first object and data for a second object. The method may additionally include comparing the first and second objects based at least in part upon a reference set and training results generated based at least in part upon the reference set and training data. The method may further include determining whether the first object and the second object are the same object based at least in part upon the comparison. Corresponding apparatuses are also provided.12-20-2012
20120328184OPTICALLY CHARACTERIZING OBJECTS - Systems and methods are provided for optically characterizing an object. A method includes querying an image search engine for the object; extracting image features from multiple images returned by the search engine in response to the query; clustering the image features extracted from the images returned by the search engine according to similarities in optical characteristics of the image features; and determining a set of image features most representative of the object based on the clustering.12-27-2012
20120281907REAL-TIME 3D POINT CLOUD OBSTACLE DISCRIMINATOR APPARATUS AND ASSOCIATED METHODOLOGY FOR TRAINING A CLASSIFIER VIA BOOTSTRAPPING - Training a strong classifier by classifying point cloud data with a first classifier, inferring a first terrain map from the classified point cloud data, reclassifying the point cloud data with the first classifier based on the first terrain map, and training a second classifier based on the point cloud data reclassified with the first classifier based on the terrain map. The point cloud data is then classified with the second classifier, and the procedure followed with the first classifier is iteratively repeated until a strong classifier is determined. A strong classifier is determined when a probability of a terrain map matching a given terrain for the strong classifier is approximately equal to a probability of a terrain map matching the given terrain for a prior trained classifier.11-08-2012
20120281910DETECTING FACIAL SIMILARITY BASED ON HUMAN PERCEPTION OF FACIAL SIMILARITY - Similar faces may be determined within images based on human perception of facial similarity. The user may provide an image including a query face to which the user wishes to find faces that are similar. Similar faces may be determined based on similarity information. Similarity information may be generated from information related to a human perception of facial similarity. Images that include faces determined to be similar, based on the similarity information, may be provided to the user as search result images. The user then may provide feedback to indicate the user's perception of similarity between the query face and the search result images.11-08-2012
20120281909LEARNING DEVICE, IDENTIFICATION DEVICE, LEARNING IDENTIFICATION SYSTEM AND LEARNING IDENTIFICATION DEVICE - A learning device includes a gradient feature extraction unit which extracts a gradient feature amount including a gradient direction at each coordinate and a gradient intensity value thereof based on an amount of variation between luminance at each coordinate of an inputted learning target pattern and luminance at a periphery thereof, a sum difference feature extraction unit which calculates a predetermined sum difference feature amount by adding the gradient intensity values according to the gradient directions included in a predetermined gradient range indicating a range of the predetermined gradient direction based on the extracted gradient feature amount and subtracting the gradient intensity values according to the gradient directions included in the other gradient range adjacent to the predetermined gradient range from the calculated sum, and a learning unit which acquires a learning parameter at each coordinate.11-08-2012
20120288187ADDITION RATIO LEARNING APPARATUS AND METHOD, IMAGE PROCESSING APPARATUS AND METHOD, PROGRAM, AND RECORDING MEDIUM - There is provided an addition ratio learning apparatus including a noise adding unit that adds noises to data of an image input as a teacher image, a motion compensating unit that sets an image where time addition noise reduction processing is executed as an NR screen and performs motion compensation with respect to the NR screen, a differential feature amount calculating unit that sets an image as an input screen and calculates a differential feature amount, a circulation history specifying unit that counts a circulation history in the time addition noise reduction processing and specifies the circulation history, an addition ratio computing unit that computes an addition ratio on the basis of pixel values, and a time adding unit that performs multiplication by a coefficient determined according to the computed addition ratio to perform weighted addition and executes the time addition noise reduction processing with respect to the input screen.11-15-2012
20100202681DETECTING DEVICE OF SPECIAL SHOT OBJECT AND LEARNING DEVICE AND METHOD THEREOF - The invention discloses a detecting device for specific subjects and a learning device and method thereof. The detecting device for specific subjects includes an input unit, one or more strong classifying units, a storage unit and a judging unit, wherein the input unit is used for inputting images to be detected; the strong classifying units are used for carrying out strong classification to the image, each strong classifying unit includes one or more weak classifying units, and the weak classifying unit carries out weak classification to the image with a weak classifying template; the storage unit stores the weak classifying template used by the weak classifying unit; and the judging unit judges whether or not the image contains specific subjects according to the classification result of the strong classifying unit. The detecting device for specific subjects also includes an incremental sample input unit and a learning unit, wherein the incremental sample input unit is used for inputting data for incremental learning, namely for inputting an incremental learning sample, which is data undetected and wrongly detected by the detecting device or other detecting devices for specific subjects; the learning unit is used for updating the weak classifying template stored in the storage unit according to the incremental learning sample inputted by the incremental sample input unit.08-12-2010
20130011051CODED APERTURE IMAGING - A method of imaging encodes light from a scene by adding projective codes expressed as a product of a known projective code matrix with a known reconstruction matrix representing an image reconstruction operation. The encoded light is detected at a photodetector. The measurements are processed by compressive sensing including projective sub-sampling to represent the measurements as a linear system. The linear system is expressed as a plurality of undetermined linear equations including a product of the known reconstruction matrix and an unknown sparse vector. The sparse vector is approximated to provide solutions to the undetermined linear equations. At least one of a reconstructed image and an exploited image is generated from the measurements using solutions to the undetermined linear equations, wherein a product of the known reconstruction matrix with the solutions to underdetermined linear equations provides an image representation of the scene of interest having N pixels, where N>k.01-10-2013
20130016899Systems and Methods for Matching Visual Object ComponentsAANM Li; YuanAACI Los AngelesAAST CAAACO USAAGP Li; Yuan Los Angeles CA USAANM Adam; HartwigAACI Marina del ReyAAST CAAACO USAAGP Adam; Hartwig Marina del Rey CA US - Systems and methods for modeling the occurrence of common image components (e.g., sub-regions) in order to improve visual object recognition are disclosed. In one example, a query image may be matched to a training image of an object. A matched region within the training image to which the query image matches may be determined and a determination may be made whether the matched region is located within an annotated image component of the training image. When the matched region matches only to the image component, an annotation associated with the component may be identified. In another example, sub-regions within a plurality of training image corpora may be annotated as common image components including associated information (e.g., metadata). Matching sub-regions appearing in many training images of objects may be down-weighted in the matching process to reduce possible false matches to query images including common image components.01-17-2013
20100172573Distinguishing Colors of Illuminated Objects Using Machine Vision - System and method for distinguishing colors of illuminated objects using machine vision. A color-balanced image that includes at least one lit area is received, as well as an indication of a region of interest that includes one of the one or more lit areas. A mask image is generated based on the region of interest. A color-balanced image of the region of interest is generated by masking the color-balanced image with the mask image, and a plurality of image attributes for the region of interest is determined by analyzing the color-balanced image of the region of interest. A color is determined based on the plurality of image attributes using a trained classifier, and the determined color stored, e.g., in a memory medium.07-08-2010
20110158511METHOD AND APPARATUS FOR FILTERING RED AND/OR GOLDEN EYE ARTIFACTS - Processing method of a digital image to filter red and/or golden eye artifacts, the digital image comprising a plurality of pixel each comprising at least one digital value represented on a plurality of bits, the method comprising: a step of selecting at least one patch of pixels of the digital image comprising pixels potentially representative of a red and/or golden eye artifact; a step of classifying the at least one patch of pixels as “eye” or “non-eye”; a step of filtering said potentially representative pixels if said patch of pixels is classified as “eye”; wherein the classifying step comprises the operations of: converting the digital values of said patch of pixels into a Gray Code representation, overall obtaining a plurality of bit maps from said patch of pixels, each bit map being associated with a respective bit of said Gray Code; an operation of individually comparing said bit maps with corresponding bit map models belonging to a patch classifier produced by a statistical analysis of bit maps obtained by converting patches of pixels of digital images containing or not red and/or golden eye artifacts into said Gray Code representation.06-30-2011
20110158510BIOLOGICALLY-INSPIRED METADATA EXTRACTION (BIME) OF VISUAL DATA USING A MULTI-LEVEL UNIVERSAL SCENE DESCRIPTOR (USD) - A computer vision system provides a universal scene descriptor (USD) framework and methodology for using the USD framework to extract multi-level semantic metadata from scenes. The computer vision system adopts the human vision system principles of saliency, hierarchical feature extraction and hierarchical classification to systematically extract scene information at multiple semantic levels.06-30-2011
20130022263System and Method for Detecting and Tracking Features in Images - A system and method for tracking features is provided which allows for the tracking of features that move in a series of images A training set of images is processed to produce clustered shape subspaces corresponding to the set of images, such that non-linear shape manifolds in the images are represented as piecewise, overlapping linear surfaces that are clustered according to similarities in perspectives. A landmark-based training algorithm (e.g., ASM) is applied to the clustered shape subspaces to train a model of the clustered shape subspaces and to create training data. A subsequent image is processed using the training data to identify features in the target image by creating an initial shape, superimposing the initial shape on the target image, and then iteratively deforming the shape in accordance with the model until a final shape is produced corresponding to a feature in the target image.01-24-2013
20120243778IMAGE RECOGNIZING APPARATUS, METHOD FOR RECOGNIZING IMAGE AND NON-TRANSITORY COMPUTER READABLE MEDIUM - An image recognizing apparatus includes a dictionary memory, a block determining module and a recognizing module. The dictionary memory stores dictionary data. The block determining module determines that a target block comprising a target pixel to be processed of a plurality of pixels in image data is a shared block to which the dictionary data is used or a mirror block to which the dictionary data to the shared block is used, based on a position of the target block. The recognizing module uses common dictionary data for the shared block and the mirror block, and recognizes a characteristic portion of the image expressed by the image data.09-27-2012
20080232682SYSTEM AND METHOD FOR IDENTIFYING PATTERNS - The present invention provides a system and method for identifying a pattern as belonging to one of a set of predetermined classes of patterns. The system comprises a plurality of classifier blocks wherein each classifier block corresponds to a distinct predetermined class of patterns and produces a mirror image of an input pattern if the input pattern belongs to the predetermined class. The system also comprises a plurality of sub-classifier blocks wherein each sub-classifier block corresponds to a distinct predetermined sub-class of a predetermined class of patterns and is coupled to a classifier block corresponding thereto for producing a mirror image of an input pattern if the pattern belongs to the predetermined sub-class. The system further comprises an input unit for capturing the pattern for identification and an output unit for displaying at least one of a mirror image of an input pattern and an identified class and sub-class of the input pattern. The system and method of the present invention may also be used as sub-modules for building large generalized learning systems.09-25-2008
20130202198Landmarks from Digital Photo Collections - Methods and systems for automatic detection of landmarks in digital images and annotation of those images are disclosed. A method for detecting and annotating landmarks in digital images includes the steps of automatically assigning a tag descriptive of a landmark to one or more images in a plurality of text-associated digital images to generate a set of landmark-tagged images, learning an appearance model for the landmark from the set of landmark-tagged images, and detecting the landmark in a new digital image using the appearance model. The method can also include a step of annotating the new image with the tag descriptive of the landmark.08-08-2013
20130170738COMPUTER-IMPLEMENTED METHOD, A COMPUTER PROGRAM PRODUCT AND A COMPUTER SYSTEM FOR IMAGE PROCESSING - The present description refers in particular to a computer-implemented method, a computer program product and a computer system for image processing, the method comprising: receiving at least one user image; identifying a plurality of image classification elements of the user image by: assigning an initial classification to the user image, wherein the initial classification is based on temporal data associated with the user image; determining at least one image label that globally describes content of the user image; calculating a label correctness value for each image label; recognizing at least one image component of the user image; calculating a component correctness value for each image component; correlating the image label and the image component using the label correctness value and the component correctness value, whereby a correlated image label and a correlated image component are identified; applying a rule to determine a category of the user image, wherein the rule is based on at least one of the following: the temporal data, the correlated image label and the correlated image component; and producing a final classification of the user image including the following image classification elements: the initial classification, the correlated image label, the correlated image component, and the category.07-04-2013
20130170739LEARNING APPARATUS, A LEARNING SYSTEM, LEARNING METHOD AND A LEARNING PROGRAM FOR OBJECT DISCRIMINATION - A learning apparatus in the present invention includes a weak discriminator generation unit that generates a weak discriminator which calculates a discrimination score of an instance of a target based on a feature and a bag label, a weak discrimination unit which calculates the discrimination score based on the generated weak discriminator, an instance probability calculation unit that calculates an instance probability of the target instance based on the calculated the discrimination score, a bag probability calculation unit that calculates a probability that no smaller than two positive instances are included in the bag based on the calculated instance probability and a likelihood calculation unit which calculates likelihood representing plausibility of the bag probability based on the bag label.07-04-2013
20110274345ACCURACY OF RECOGNITION BY MEANS OF A COMBINATION OF CLASSIFIERS - In one embodiment, there is provided a method for an Optical Character Recognition (OCR) system. The method comprises: recognizing an input character based on a plurality of classifiers, wherein each classifier generates an output by comparing the input character with a plurality of trained patterns; grouping the plurality of classifiers based on a classifier grouping criterion; and combining the output of each of the plurality of classifiers based on the grouping.11-10-2011
20130142422IMAGE PROCESSING METHOD, AND IMAGE PROCESSOR - Included are (a) performing processes on second training data items stored in a training database to generate third training data items each obtained through a corresponding one of the processes, (b) selecting, from among the third training data items generated in step (a), a selection data item having a highest similarity to a feature data item of the input image, (c) generating a high-frequency data item by: determining (i) the second training data item used in generating the selection data item and (ii) a first process performed on the second training data item to generate the selection data item; and performing the first process on the first training data item that is paired with the determined second training data item; and (d) generating an output image by adding an image indicated by the high-frequency data item to the input image.06-06-2013
20130114888IMAGE PROCESSING APPARATUS, COMPUTER PROGRAM PRODUCT, AND IMAGE PROCESSING METHOD - According to an embodiment, an image processing apparatus includes a feature data calculator, a generating unit, and an adding unit. The feature data calculator calculates feature data representing changes in pixel values within a first range of an input image. The generating unit obtains a weight of a predetermined image pattern on the basis of a probability distribution and the feature data. The weight represents a pattern of changes in the pixel values. The probability distribution represents a distribution of relative values of feature data of a learning image containing a high-frequency component with respect to feature data of a learning image. The generating unit weights the predetermined image pattern with the weight so as to generate a high-frequency component with respect to the input image. The adding unit adds the high-frequency component to the input image.05-09-2013
20130129200DEVICE FOR SETTING IMAGE ACQUISITOIN CONDITIONS, AND COMPUTER PROGRAM - The present invention relates to a device (05-23-2013
20130129198SMART 3D PACS WORKFLOW BY LEARNING - Methods and systems to provide a hanging protocol including three-dimensional manipulation for display of clinical images in an exam are disclosed. An example method includes detecting selection of a new image exam for display by a user. The example method includes automatically identifying at least one of a) a previously learned hanging protocol saved for the user and b) a saved hanging protocol associated with a prior image exam corresponding to the new image exam. The example method includes applying the saved hanging protocol to the new image exam, the saved hanging protocol including three-dimensional manipulation to be automatically applied to the new image exam as part of the hanging protocol configuration for display. The example method includes facilitating display of the new image exam based on the saved hanging protocol.05-23-2013
20130142417SYSTEM AND METHOD FOR AUTOMATICALLY DEFINING AND IDENTIFYING A GESTURE - A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.06-06-2013
20130142418RANKING AND SELECTING REPRESENTATIVE VIDEO IMAGES - Techniques are described herein for selecting representative images for video items using a trained machine learning engine. A training set is fed to a machine learning engine. The training set includes, for each image in the training set, input parameter values and an externally-generated score. Once a machine learning model has been generated based on the training set, input parameters for unscored images are fed to the trained machine learning engine. Based on the machine learning model, the trained machine learning engine generates scores for the images. To select a representative image for a particular video item, candidate images for that particular video item may be ranked based on their scores, and the candidate image with the top score may be selected as the representative image for the video item.06-06-2013
20130142424Optical Pattern Recognition Technique - Disclosed is a distortion invariant system, method and computer readable medium for detecting the presence of one or more predefined targets in an input image. The input image and a synthetic discriminant function (SDF) reference image are correlated in a shift phase-encoded fringe-adjusted joint transform correlation (SPFJTC) correlator yielding a correlation output. A peak-to-clutter ratio (PCR) is determined for the correlation output and compared to a threshold value. A predefined target is present in the input image when the PCR is greater than or equal to the threshold value.06-06-2013
20130142420IMAGE RECOGNITION INFORMATION ATTACHING APPARATUS, IMAGE RECOGNITION INFORMATION ATTACHING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - An image recognition information attaching apparatus includes a retrieving unit that retrieves image information on a per piece basis of identification information, from the image information having the identification information associated thereto in advance, a generator unit that generates feature information from the image information retrieved by the retrieving unit, and a learning unit that provides a learning result by learning a relation between the feature information generated by the generator unit and the identification information of the image information corresponding to the feature information, using a stochastic model including a mixture of a plurality of probability distributions.06-06-2013
20130142423IMAGE CLUSTERING USING A PERSONAL CLOTHING MODEL - The disclosure relates to a system and a method for generating clothing feature data representative of at least one clothing feature of a piece of clothing being worn by the person in a set of images, and training a discriminative clothing classifier using the clothing feature data to provide a personal clothing model that corresponds to the piece of clothing. The personal clothing model can be used to identify additional images in which the person appears.06-06-2013
20110222759INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing apparatus includes a characteristic amount calculating unit calculating a characteristic amount for each of a plurality of n different image patterns, a specifying unit specifying a best-matching image pattern among the plurality of n image patterns for each of frames forming a learning moving picture and having temporal continuity, a computing unit computing a collocation probability Pij indicating a probability that, for a frame located at a position where a temporal distance to a frame for which a first image pattern Xi is specified among the plurality of n image patterns is within a predetermined threshold τ, a second image pattern Xj is specified among the plurality of n image patterns, and a grouping unit grouping the plurality of n image patterns by using the computed collocation probability Pij.09-15-2011
20110229019Scene Adaptive Brightness/Contrast Enhancement - A method for brightness and contrast enhancement includes computing a luminance histogram of a digital image, computing first distances from the luminance histogram to a plurality of predetermined luminance histograms, estimating first control point values for a global tone mapping curve from predetermined control point values corresponding to a subset of the predetermined luminance histograms selected based on the computed first distances, and interpolating the estimated control point values to determine the global tone mapping curve. The method may also include dividing the digital image into a plurality of image blocks, and enhancing each pixel in the digital image by computing second distances from a pixel in an image block to the centers of neighboring image blocks, and computing an enhanced pixel value based on the computed second distances, predetermined control point values corresponding to the neighboring image blocks, and the global tone mapping curve.09-22-2011
20110235901METHOD, APPARATUS, AND PROGRAM FOR GENERATING CLASSIFIERS - Classifiers, which are combinations of a plurality of weak classifiers, for discriminating objects included in detection target images by employing features extracted from the detection target images to perform multi class discrimination including a plurality of classes regarding the objects are generated. When the classifiers are generated, branching positions and branching structures of the weak classifiers of the plurality of classes are determined, according to the learning results of the weak classifiers in each of the plurality of classes.09-29-2011
20110235900Method for Training Multi-Class Classifiers with Active Selection and Binary Feedback - A multi-class classifier is trained by selecting a query image from a set of active images based on a membership probability determined by the classifier, wherein the active images are unlabeled. A sample image is selected from a set of training image based on the membership probability of the query image, wherein the training images are labeled. The query image and the sample images are displayed to a user on an output device. A response from the user is obtained with an input device, wherein the response is a yes-match or a no-match. The query image with the label of the sample image is added to the training set if the yes-match is obtained, and otherwise repeating the selecting, displaying, and obtaining steps until a predetermined number of no-match is reached to obtain the multi-class classifier.09-29-2011
20130148880Image Cropping Using Supervised Learning - Software for supervised learning extracts a set of pixel-level features from each source image in collection of source images. Each of the source images is associated with a thumbnail created by an editor. The software also generates a collection of unique bounding boxes for each source image. And the software calculates a set of region-level features for each bounding box. Each region-level feature results from the aggregation of pixel values for one of the pixel-level features. The software learns a regression model, using the calculated region-level features and the thumbnail associated with the source image. Then the software chooses a thumbnail from a collection of unique bounding boxes in a new image, based on application of the regression model.06-13-2013
20130148881Image Classification - The present disclosure introduces a method and an apparatus for classifying images. Classification image features of an image for classification are extracted. Based on a similarity relationship between each classification image feature and one or more visual words in a pre-generated visual dictionary, each classification image feature is quantified by multiple visual words in the visual dictionary and a similarity coefficient between each classification image feature and each of the visual words is determined. Based on the similarity coefficient of each visual word that corresponds to different classification image features, a weight of each visual word is determined to establish a classification visual word histogram of the image for classification. The classification visual word histogram is input into an image classifier that is trained by sample visual word histograms arising from multiple sample images. An output result is used to determine a classification of the image for classification.06-13-2013
20120275693METHOD FOR IDENTIFYING MARKED CONTENT - Briefly, in accordance with one embodiment, a method of identifying marked content is described.11-01-2012
20120275692RECOGNITION APPARATUS, RECOGNITION METHOD, AND STORAGE MEDIUM - A recognition apparatus includes a calculation unit configured to calculate likelihood of each feature quantity based on the weighted distribution of the feature quantity extracted from a plurality of learning images, a correction unit configured, if a ratio of a learning image to a specific feature quantity is equal to or smaller than a predetermined ratio and a weight for the specific feature quantity is greater than a predetermined value, to correct the value of likelihood of the specific feature quantity to lower the value based on the distribution, a setting unit configured to set the likelihood corrected by the correction unit in association with a feature quantity, and a discrimination unit to extract a feature quantity from an input image and discriminate whether the input image includes a predetermined object based on the likelihood associated with the feature quantity.11-01-2012
20120275691COEFFICIENT LEARNING DEVICE AND METHOD, IMAGE PROCESSING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM - A feature-quantity extraction unit extracts a feature quantity of a target pixel of a student image. The target pixel is classified into a predetermined class. Natural-image processing of the target pixel is performed. Artificial-image processing of the target pixel is performed. A sample of a normal equation is generated using a pixel value of the target pixel subjected to the natural-image processing, a pixel value of the target pixel subjected to the artificial-image processing, a pixel value of a target pixel of a teacher image, and a predetermined mixing coefficient for each class. The mixing coefficient is calculated on the basis of a plurality of generated samples.11-01-2012
20130156297Learning Image Processing Tasks from Scene Reconstructions - Learning image processing tasks from scene reconstructions is described where the tasks may include but are not limited to: image de-noising, image in-painting, optical flow detection, interest point detection. In various embodiments training data is generated from a 2 or higher dimensional reconstruction of a scene and from empirical images of the same scene. In an example a machine learning system learns at least one parameter of a function for performing the image processing task by using the training data. In an example, the machine learning system comprises a random decision forest. In an example, the scene reconstruction is obtained by moving an image capture apparatus in an environment where the image capture apparatus has an associated dense reconstruction and camera tracking system.06-20-2013
20130156298Using High-Level Attributes to Guide Image Processing - Using high-level attributes to guide image processing is described. In an embodiment high-level attributes of images of people such as height, torso orientation, body shape, gender are used to guide processing of the images for various tasks including but not limited to joint position detection, body part classification, medical image analysis and others. In various embodiments one or more random decision forests are trained using images where global variable values such as player height are known in addition to ground-truth data appropriate for the image processing task concerned. In some examples sequences of images are used where global variables are static or vary smoothly over the sequence. In some examples one or more trained random decision forests are used to find global variable values as well as output values for the task concerned such as joint positions or body part classes.06-20-2013
20130156299METHOD AND APPARATUS FOR DETECTING PEOPLE WITHIN VIDEO FRAMES BASED UPON MULTIPLE COLORS WITHIN THEIR CLOTHING - A video analytic device performs a method for detecting people within frames of video based upon multiple colors within their clothing. The method includes: receiving a frame of video; and determining that a first color region within the frame matches a first color of interest for a clothing uniform, wherein the determining is based on a first set of color representation constraints. The method further includes determining that a second color region within the frame matches a second color of interest for the clothing uniform, wherein the determining is based on a second set of color representation constraints and the first and second colors of interest are different. In addition, the method includes applying a set of geometric constraints to the first and second color regions to determine a count of people within the frame wearing the clothing uniform.06-20-2013
20130156300Multi-Class Classification Method - A test sample is classified by determining a nearest subspace residual from subspaces learned from multiple different classes of training samples, and a collaborative residual from a collaborative representation of a dictionary constructed from all of the test samples. The residuals are used to determine a regularized residual. The subspaces, the dictionary and the regularized residual are inputted into a classifier, wherein the classifier includes a collaborative representation classifier and a nearest subspace classifier, and a label is assigned to the test sample using the classifier, and wherein the regularization parameter balances a trade-off between the collaborative representation classifier the nearest subspace classifier.06-20-2013
20130156301METHOD AND SYSTEM FOR RECOGNIZING IMAGES - A method and a system for recognizing at least one testing image according to classes are provided, wherein each of the classes includes sample images. The method includes generating an average image of each class according to the sample images, generating a feature enhancement mask according to differences between the average images of the classes, enhancing the sample images of each class by using the feature enhancement mask to generate a plurality of enhanced sample images corresponding to each class, and training a classifier according to the enhanced sample images of each class. The method also includes enhancing the at least one testing image by using the feature enhancement mask to generate an enhanced testing image, classifying the enhanced testing image into one of the classes by using the classifier, and recognizing that the testing image belongs to the classified class. Thereby, this method can effectively recognize the testing image.06-20-2013
20130156302HANDWRITTEN WORD SPOTTER SYSTEM USING SYNTHESIZED TYPED QUERIES - A wordspotting system and method are disclosed for processing candidate word images extracted from handwritten documents. In response to a user inputting a selected query string, such as a word to be searched in one or more of the handwritten documents, the system automatically generates at least one computer-generated image based on the query string in a selected font or fonts. A model is trained on the computer-generated image(s) and is thereafter used in the scoring the candidate handwritten word images. The candidate or candidates with the highest scores and/or documents containing them can be presented to the user, tagged, or otherwise processed differently from other candidate word images/documents.06-20-2013
20130156303IMAGE PROCESSING APPARATUS AND IMAGE PROCESSING METHOD - An image processing method is provided for an image processing apparatus which executes processing by allocating a plurality of weak discriminators to form a tree structure having branches corresponding to types of objects so as to detect objects included in image data. Each weak discriminator calculates a feature amount to be used in a calculation of an evaluation value of the image data, and discriminates whether or not the object is included in the image data by using the evaluation value. The weak discriminator allocated to a branch point in the tree structure further selects a branch destination using at least some of the feature amounts calculated by weak discriminators included in each branch destination.06-20-2013
20130156304METHOD FOR CLASSIFICATION OF VIDEOS - A method for classifying a video regarding a subjective characteristic, the method comprising: 06-20-2013
20120281908INTELLIGENT AIRFOIL COMPONENT SURFACE IMAGING INSPECTION - A method for inspecting surfaces including acquiring a surface image from a surface of a component; providing an image registration for the surface image; inspecting the component in response to the image registration to produce an input data set; creating an output data set in response to the input data set utilizing a fuzzy logic algorithm; and identifying a surface feature in response to the surface image and the output data set where acquiring the surface image further includes generating a radiation media; directing the radiation media at the component; detecting a responding radiation media in response to the directed radiation media and the component; creating the surface image in response to detecting the responding radiation media; and adjusting the generation of the radiation media in response to the surface image and a standard image.11-08-2012
20130182946METHODS AND SYSTEM FOR ANALYZING AND RATING IMAGES FOR PERSONALIZATION - As set forth herein, a computer-implemented method facilitates pre-analyzing an image and automatically suggesting to the user the most suitable regions within an image for text-based personalization. Image regions that are spatially smooth and regions with existing text (e.g. signage, banners, etc.) are primary candidates for personalization. This gives rise to two sets of corresponding algorithms: one for identifying smooth areas, and one for locating text regions. Smooth regions are found by dividing the image into blocks and applying an iterative combining strategy, and those regions satisfying certain spatial properties (e.g. size, position, shape of the boundary) are retained as promising candidates. In one embodiment, connected component analysis is performed on the image for locating text regions. Finally, based on the smooth and text regions found in the image, several alternative approaches are described herein to derive an overall metric for “suitability for personalization.”07-18-2013
20130182947APPARATUS AND METHOD FOR ESTIMATING POSE OF OBJECT - An apparatus and method for estimating a pose of an object are provided. The apparatus includes an object input unit configured to input an object in an object tracking unit and an object identifying unit, an object tracking unit configured to obtain a tracked pose probability density of the object based on a tracking scheme, an object identifying unit configured to obtain an identified pose probability density of the object based on a training model, and a combination unit configured to obtain an estimated pose probability density of the object using a combination of the tracked pose probability density and the identified pose probability density and to estimate a pose of the object based on the estimated pose probability density of the object. Through the combination, a cumulative error occurring in the object tracking may be corrected, resulting in more accurate object estimation.07-18-2013
20130182948Method and Apparatus for Training a Probe Model Based Machine Vision System - A method for training a pattern recognition algorithm including the steps of identifying the known location of the pattern that includes repeating elements within a fine resolution image, using the fine resolution image to train a model associated with the fine image, using the model to examine the fine image resolution image to generate a score space, examining the score space to identify a repeating pattern frequency, using a coarse image that is coarser than the finest image resolution image to train a model associated with the coarse image, using the model associated with the coarse image to examine the coarse image thereby generating a location error, comparing the location error to the repeating pattern frequency and determining if the coarse image resolution is suitable for locating the pattern within a fraction of one pitch of the repeating elements.07-18-2013
20120288186SYNTHESIZING TRAINING SAMPLES FOR OBJECT RECOGNITION - An enhanced training sample set containing new synthesized training images that are artificially generated from an original training sample set is provided to satisfactorily increase the accuracy of an object recognition system. The original sample set is artificially augmented by introducing one or more variations to the original images with little to no human input. There are a large number of possible variations that can be introduced to the original images, such as varying the image's position, orientation, and/or appearance and varying an object's context, scale, and/or rotation. Because there are computational constraints on the amount of training samples that can be processed by object recognition systems, one or more variations that will lead to a satisfactory increase in the accuracy of the object recognition performance are identified and introduced to the original images.11-15-2012
20130195351IMAGE PROCESSOR, IMAGE PROCESSING METHOD, LEARNING DEVICE, LEARNING METHOD AND PROGRAM - Disclosed herein is an image processor including: a feature point extraction section adapted to extract the feature points of an input image; a correspondence determination section adapted to determine the correspondence between the feature points of the input image and those of a reference image using a feature point dictionary; a feature point coordinate distortion correction section adapted to correct the coordinates of the feature points of the input image corresponding to those of the reference image; a projection relationship calculation section adapted to calculate the projection relationship between the input and reference images; a composite image coordinate transform section adapted to generate a composite Image to be attached from a composite image; and an output image generation section adapted to merge the input image with the composite image to be attached.08-01-2013
20130202199USING HIGHER ORDER STATISTICS TO ESTIMATE PIXEL VALUES IN DIGITAL IMAGE PROCESSING TO IMPROVE ACCURACY AND COMPUTATION EFFICIENCY - A method, system and computer program product for improving accuracy and computation efficiency in interpolation, upsampling and color channel estimation. A Bayesian estimator used to estimate the value of a pixel in an image is constructed using measurements of high-order (e.g., 3rd, 4th, 5th) statics for nearby points in natural images. These measurements reveal highly systematic statistical regularities that were ignored from the prior algorithms due to their restrictive measurements and assumptions. As a result, the accuracy in interpolation, upsampling and color channel prediction is improved. Furthermore, the process for constructing a Bayesian estimator is simpler and more direct by storing in a table the mean value of the pixel value to be estimated for each combination of values of nearby points in training samples. As a result of having a simpler and more direct approach than existing methods, the computational efficiency is improved.08-08-2013
20120063674PATTERN RECOGNITION APPARATUS AND METHOD THEREFOR CONFIGURED TO RECOGNIZE OBJECT AND ANOTHER LOWER-ORDER OBJECT - In a pattern recognition apparatus, a characteristic amount calculation unit calculates a characteristic amount for recognizing a desired object from a partial image clipped from an input pattern, a likelihood calculation unit calculates a likelihood of an object as a recognition target from the characteristic amount calculated by the characteristic amount calculation unit by referring to an object dictionary, and an object determination unit determines whether the partial image is the object as the recognition target based on the likelihood of the object calculated by the likelihood calculation unit. The likelihood calculation unit calculates the likelihood of the object as the recognition target from the characteristic amount calculated by the characteristic amount calculation unit by referring to a specific object dictionary. The object determination unit determines whether the partial image is a specific object as the recognition target from the likelihood of the object calculated by the likelihood calculation unit.03-15-2012
20120087575RECOGNIZING HAND POSES AND/OR OBJECT CLASSES - There is a need to provide simple, accurate, fast and computationally inexpensive methods of object and hand pose recognition for many applications. For example, to enable a user to make use of his or her hands to drive an application either displayed on a tablet screen or projected onto a table top. There is also a need to be able to discriminate accurately between events when a user's hand or digit touches such a display from events when a user's hand or digit hovers just above that display. A random decision forest is trained to enable recognition of hand poses and objects and optionally also whether those hand poses are touching or not touching a display surface. The random decision forest uses image features such as appearance, shape and optionally stereo image features. In some cases, the training process is cost aware. The resulting recognition system is operable in real-time.04-12-2012
20120087574LEARNING DEVICE, LEARNING METHOD, IDENTIFICATION DEVICE, IDENTIFICATION METHOD, AND PROGRAM - Provided is a learning device including: an acquisition section that acquires a plurality of image pairs in which the same subjects appear and a plurality of image pairs in which different subjects appear; a setting section that sets feature points on one image and the other image of each image pair; a selection section that selects a plurality of prescribed feature points, which are set at the same positions of the one image and the other image, so as to thereby select a feature extraction filter for each prescribed feature point; an extraction section that extracts the features of the prescribed feature points of each of the one image and the other image by using the plurality of feature extraction filters; a calculation section that calculates a correlation between the features; and a learning section that learns a same-subject classifier on the basis of the correlation and label information.04-12-2012
20130208977RECEPTIVE FIELD LEARNING FOR POOLED IMAGE FEATURES - Systems and methods are disclosed for image classification by receiving an overcomplete set of spatial regions, jointly optimizing the classifier and the pooling region for each pooled feature; and performing incremental feature selection and retraining using a grafting process to efficiently train the classifier.08-15-2013
20130208978CONTINUOUS CHARTING OF NON-UNIFORMITY SEVERITY FOR DETECTING VARIABILITY IN WEB-BASED MATERIALS - A computerized inspection system is described for detecting the presence of non-uniformity defects in a manufactured web material and for providing output indicative of a severity level of each defect. The system provides output that provides the severity levels of the non-uniformity defects in real-time on a continuous scale. Training software processes a plurality of training samples to generate a model, where each of the training samples need only be assigned one of a set of discrete rating labels for the non-uniformity defects. The training software generates the model to represent a continuous ranking of the training images, and the inspection system utilizes the model to compute the severity levels of the web material on a continuous scale in real-time without limiting the output to the discrete rating labels assigned to the training samples.08-15-2013

Patent applications in class Trainable classifiers or pattern recognizers (e.g., adaline, perceptron)

Patent applications in all subclasses Trainable classifiers or pattern recognizers (e.g., adaline, perceptron)