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LEARNING SYSTEMS

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382 - Image analysis

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Class / Patent application numberDescriptionNumber of patent applications / Date published
382159000 Trainable classifiers or pattern recognizers (e.g., adaline, perceptron) 307
382156000 Neural networks 26
Entries
DocumentTitleDate
20130077855SYSTEMS AND METHODS FOR PROCESSING DOCUMENTS OF UNKNOWN OR UNSPECIFIED FORMAT - A computer implemented method for extracting meaningful text from a document of unknown or unspecified format. In a particular embodiment, the method includes reading the document, thereby to extract raw encoded text, analysing the raw encoded text, thereby to identify one or more text chunks, and for a given chunk, performing compression identification analysis to determine whether compression is likely and, in the event that compression. The method can further include performing a decompression process, performing an encoding identification process thereby to identify a likely character encoding protocol, and converting the chunk using the identified likely character encoding protocol, thereby to output the chunk as readable text.03-28-2013
20090154795INTERACTIVE 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.06-18-2009
20120114226IMAGE PROCESSING DEVICE AND METHOD, DATA PROCESSING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM - A tentative eigenprojection matrix (#05-10-2012
20130011049IMAGE PROCESSING APPARATUS, METHOD, AND PROGRAM - The present invention relates to an image processing apparatus, method, and program that can extract an object from an input image more easily and more accurately.01-10-2013
20090161947IMAGE PROCESSING DEVICE AND METHOD, LEARNING DEVICE AND METHOD, PROGRAM, AND RECORDING MEDIUM - An image processing device includes: a smoothing section configured to extract a smoothing tap and smooth a target image on the basis of pixel values within the tap, the smoothing tap being of variable size and including plural pixels centered on each target pixel of the image; a class tap extracting section configured to extract a class tap including plural pixels centered on each target pixel in the smoothed image; a class code determining section configured to generate a code corresponding to a characteristic of variation of pixel values within the class tap, and determine a class code including a size of the smoothing tap and the code; and a pixel value computing section configured to read tap coefficients corresponding to the determined class code, and multiply pixel values forming a prediction tap extracted from the smoothed image, by the tap coefficients to calculate pixel values of a processed image.06-25-2009
20100080449Learning Method for Article Storage Facility - A learning method is disclosed for an article storage facility having an article storage rack including article storage units arranged in a rack lateral width direction and a vertical direction, a vertically movable lift, and a horizontal travel carriage associated with the vertically movable lift. A frontal view camera is positioned with respect to the article transfer device such as to capture an image of a detected member provided for each of the storage units from a rack fore-and-aft direction. An angular view camera is positioned with respect to the article transfer device such as to be displaced relative to the frontal view camera in the rack lateral width direction or the vertical direction and such as to capture an image of a detected member from a direction at an angle relative to the rack fore-and-aft direction. And vertical direction correction information, rack lateral width correction information and extending and retracting distance correction information are derived based from image information.04-01-2010
20120106834BACKGROUND MODEL LEARNING SYSTEM FOR LIGHTING CHANGE ADAPTATION UTILIZED FOR VIDEO SURVEILLANCE - Surveillance systems often encounter great challenges from lighting variations, especially for those inspecting outdoor environments. To construct a surveillance system robust to various background scene changes, including lighting variations, a strategy of background model learning is widely adopted. Based on this strategy, many approaches have been proposed in decades to represent background scenes by statistical models and to adapt background changes over time into the models. However, the focus of most background model learning research is put on adaptation of scene vibrations in to background, as well as of gradual lighting variations. For the background model adaptation to drastic lighting changes, many background model learning approaches are often inefficient. As a result, false alarms in foreground detection are issued under such quick lighting changes. To suppress this kind of false alarms, a new system design of background model learning is proposed.05-03-2012
20090285472DATA PROCESSING APPARATUS AND DATA PROCESSING METHOD - A data processing apparatus processes input data and outputs the processed data. The data processing apparatus includes a data processing section and a real-time learning section. The data processing section processes the input data by a predetermined processing method and outputs the processed data. The real-time learning section controls such that the processing method is learned in real time and the data processing section processes the input data by the learned processing method, so that the output data is improved as time elapses.11-19-2009
20100278419INFORMATION PROCESSING APPARATUS AND METHOD, AND PROGRAM - An information processing apparatus includes a feature amount extraction unit extracting a feature amount of each frame of an image, a maximum likelihood state series estimation unit estimating maximum likelihood state series using the feature amount, a highlight label generation unit generating highlight label series with respect to the attention detector learning content, and a learning unit learning the highlight detector that is the state transition probability model using learning label series that is a pair of the maximum likelihood state series obtained from the attention detector learning content and the highlight label series.11-04-2010
20120294513IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, PROGRAM, STORAGE MEDIUM, AND LEARNING APPARATUS - A prediction calculation unit calculates a pixel value of a pixel of interest for each color component by a calculation of a learned predictive coefficient and a predictive tap, and outputs an output image including the pixel value of the pixel of interest of each color component. For example, the present technology can be applied to an image processing apparatus.11-22-2012
20120294512LEARNING APPARATUS AND METHOD, IMAGE PROCESSING APPARATUS AND METHOD, PROGRAM, AND RECORDING MEDIUM - There is provided an image processing apparatus including a model-based processing unit that executes model-based processing for converting resolution and converting an image on the basis of a camera model and a predetermined model having aligning, with respect to a high-resolution image output one frame before, and a prediction operation unit that performs a prediction operation on a pixel value of a high-resolution image to be output, on the basis of parameters stored in advance, an observed low-resolution image that is an input low-resolution image, and an image obtained by executing the model-based processing.11-22-2012
20120294511EFFICIENT RETRIEVAL OF ANOMALOUS EVENTS WITH PRIORITY LEARNING - Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.11-22-2012
20110229016INTRODUCTION SYSTEM, METHOD OF INTRODUCTION, AND INTRODUCTION PROGRAM - An introduction system is capable of identifying, with a high degree of precision, applicants who fulfill recruiter's requirements. An applicant identification unit 09-22-2011
20110026810IMAGE ANALYZING APPARATUS, IMAGE ANALYZING METHOD, AND COMPUTER READABLE MEDIUM - Provided is an image analyzing apparatus for efficiently performing detection of an object and tracking of a specified object, including a feature value recording section that records a plurality of reference feature values different in type from each other; a feature value extracting section that extracts a plurality of feature values different in type from each other, from each of a plurality of moving image constituent images included in a moving image; an object extracting section that extracts an object from the moving image constituent images, based on a degree of matching of the plurality of extracted feature values with respect to the plurality of reference feature values recorded in the feature value recording section; a reference feature value calculating section that calculates, from the plurality of reference feature values recorded in the feature value recording section, a plurality of reference feature values adjusted to the feature values of the extracted object, to a predetermined degree corresponding to the type; and a feature value updating section that updates the plurality of reference feature values recorded in the feature value recording section, with the plurality of reference feature values calculated by the reference feature value calculating section.02-03-2011
20100303342FINDING ICONIC IMAGES - Iconic images for a given object or object category may be identified in a set of candidate images by using a learned probabilistic composition model to divide each candidate image into a most probable rectangular object region and a background region, ranking the candidate images according to the maximal composition score of each image, removing non-discriminative images from the candidate images, clustering highest-ranked candidate images to form clusters, wherein each cluster includes images having similar object regions according to a feature match score, selecting a representative image from each cluster as an iconic image of the object category, and causing display of the iconic image. The composition model may be a Naïve Bayes model that computes composition scores based on appearance cues such as hue, saturation, focus, and texture. Iconic images depict an object or category as a relatively large object centered on a clean or uncluttered contrasting background.12-02-2010
20110038531LEARNING STRING TRANSFORMATIONS FROM EXAMPLES - Techniques are described to leverage a set of sample or example matched pairs of strings to learn string transformation rules, which may be used to match data records that are semantically equivalent. In one embodiment, matched pairs of input strings are accessed. For a set of matched pairs, a set of one or more string transformation rules are learned. A transformation rule may include two strings determined to be semantically equivalent. The transformation rules are used to determine whether a first and second string match each other.02-17-2011
20110044533VISUALIZING AND UPDATING LEARNED EVENT MAPS IN SURVEILLANCE SYSTEMS - Techniques are disclosed for visually conveying an event map. The event map may represent information learned by a surveillance system. A request may be received to view the event map for a specified scene. The event map may be generated, including a background model of the specified scene and at least one cluster providing a statistical distribution of an event in the specified scene. Each statistical distribution may be derived from data streams generated from a sequence of video frames depicting the specified scene captured by a video camera. Each event may be observed to occur at a location in the specified scene corresponding to a location of the respective cluster in the event map. The event map may be configured to allow a user to view and/or modify properties associated with each cluster. For example, the user may label a cluster and set events matching the cluster to always (or never) generate an alert.02-24-2011
20120308122FAST METHODS OF LEARNING DISTANCE METRIC FOR CLASSIFICATION AND RETRIEVAL - A nearest-neighbor-based distance metric learning process includes applying an exponential-based loss function to provide a smooth objective; and determining an objective and a gradient of both hinge-based and exponential-based loss function in a quadratic time of the number of instances using a computer.12-06-2012
20120308121IMAGE RANKING BASED ON ATTRIBUTE CORRELATION - Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may then be searched via the trained attribute detectors for images comprising attributes in a multi-attribute query, wherein images are retrieved from the searching that each comprise one or more of the query attributes and also in response to information from the trained attribute detectors corresponding to attributes that are not a part of the query but are relevant to the query attributes as a function of the learned plurality of pair-wise correlations. The retrieved images are ranked as a function of respective total numbers of attributes within the query subset attributes.12-06-2012
20120008858IMAGE PROCESSING - An image segmentation method has a training phase and a segmentation phase. In the training phase, a frame of pixellated data from a camera is processed using information on camera characteristics to render it camera independent. The camera independent data are processed using a chosen value of illuminant spectral characteristics to derive reflectivity data of the items in the image. Pixels of high reflectivity are established. Then, using data from the high reflectivity pixels, the actual illuminant spectral characteristics are established. The illuminant data are then processed to determine information on the illumination of the scene represented by the frame of pixellated data to derive reflectivity data of the scene. The segmentation phase comprises operating on a subsequent frame of pixellated data to render it camera independent and using the determined illumination information to process the camera independent data to determine reflectivity data of the scene to derive a foreground mask.01-12-2012
20120250981INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM - An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.10-04-2012
20120134576AUTOMATIC RECOGNITION OF IMAGES - Presented is a method of automatically performing an action, based on graphical input. The method comprises: receiving, for a user, an input image; comparing the input image with the contents of a user-customized database comprising a plurality of records, each record representing a predefined class of image, wherein the user has previously associated records in the database with respective specified actions; attempting to recognize the image, based on the similarity of the input image to one of the predefined classes of image represented in the user-customised database; and if the image is recognized, performing the action previously associated by the user with the class. Also presented is apparatus for recognizing an image and a method of constructing a user-customized database.05-31-2012
20120257818SYSTEMS AND METHODS FOR DATA FUSION MAPPING ESTIMATION - Systems and methods are disclosed for generating a probability density to estimate the probability that an event will occur in a region of interest. The methods input spatial event data comprising one or more events occurring in the region of interest along with auxiliary data related to the region of interest. The auxiliary data comprises non-event data having spatial resolution such that the probability density estimate for the region of interest is calculated based on a function of the auxiliary data and the event data. In particular, the auxiliary data is used to generate a penalty functional used in the calculation of the probability density estimate.10-11-2012
20120263375METHOD AND DEVICE FOR SELECTING OPTIMAL TRANSFORM MATRICES FOR DOWN-SAMPLING DCT IMAGE - Down-sampling of an image may be performed in the DCT domain. Transform matrices are obtained for down-sampling a DCT image of size M×N to a down-sampled DCT image of size I×J. The transform matrices may be used to down-sample the DCT image directly in the DCT domain. A spatial domain down-sampling method is selected and applied to the DCT image to produce a down-sampled DCT reference image. The transform matrices are selected by solving an optimization problem, leading to transform matrices which achieve a desired trade-off between the visual quality of images obtained using the transform matrices and the computational complexity associated with using the transform matrices. The visual quality is a measure of the difference between the down-sampled DCT image obtained using the transform matrices and the visual quality of the DCT reference image obtained using a spatial domain down-sampling method.10-18-2012
20110123100Predicting States of Subjects - Methods for predicting states of a subject are presented. For example, a method for predicting states of a subject includes obtaining training data comprising a plurality of variables, obtaining training states associated with the training data, and forming a predictive model according to the training data and the training states, the predictive model predictive of the training states. The forming of the predictive model includes extracting one or more hidden components from the training data. The extracting of the one or more hidden components includes regression analysis including determining one or more relationships between the one or more hidden components and the plurality of variables, and determining one or more relationships between the one or more hidden components and the training states. A number of the one or more hidden components is less than a number of the plurality of variables and greater than a number of the training states.05-26-2011
20100254594SKETCH GENERATING SYSTEM AND METHOD FOR GENERATING SKETCH BASED ON IMAGE - A sketch generating system and a method for generating a sketch based on an image are provided. The system includes: a sketch database and a generating subsystem. The sketch database stores local image samples and corresponding local sketch units in different categories. The generating subsystem extracts geometrical features from an input image, retrieves local image units from the input image according to the geometrical features; as to each local image unit retrieved, searches the sketch database for a local sketch unit corresponding to a local image sample having a largest similarity value with the local image unit, and combines all local sketch units found to form one sketch.10-07-2010
20120328183METHOD AND DEVICE FOR SELECTING TRANSFORM MATRICES FOR DOWN-SAMPLING DCT IMAGE USING LEARNING WITH FORGETTING ALGORITHM - Down-sampling of an image may be performed in the DCT domain. A multiple layered network is used to select transform matrices for down-sampling a DCT image of size M×N to a DCT image of size I×J. A spatial domain down-sampling method is selected and applied to the DCT image to produce a down-sampled DCT reference image. A learning with forgetting algorithm is used to apply a decay to the elements of the transform matrix and select a transform matrices which solve an optimization problem. The optimization problem is a function of the visual quality of images obtained using the transform matrices and the computational complexity associated with using the transform matrices. The visual quality is a measure of the difference between the down-sampled DCT image obtained using the transform matrices and the visual quality of the DCT reference image obtained using a spatial domain down-sampling method.12-27-2012
20110274344Systems and methods for manifold learning for matting - Systems for manifold learning for matting are disclosed, with methods and processes for making and using the same. The embodiments disclosed herein provide a closed form solution for solving the matting problem by a manifold learning technique, Local Linear Embedding. The transition from foreground to background is characterized by color and texture variations, which should be captured in the alpha map. This intuition implies that neighborhood relationship in the feature space should be preserved in the alpha map. By applying Local Linear Embedding using the disclosed embodiments, the local image variations can be preserved in the embedded manifold, which is the resulting alpha map. Without any strong assumption, such as color line model, the disclosed embodiments can be easily extended to incorporate other features beyond RGB color features, such as gradient and texture information.11-10-2011
20130094756METHOD AND SYSTEM FOR PERSONALIZED ADVERTISEMENT PUSH BASED ON USER INTEREST LEARNING - Embodiments of the present invention relate to a method and a system for personalized advertisement push based on user interest learning. The method may include: obtaining multiple user interest models through multitask sorting learning; extracting an object of interest in a video according to the user interest models; and extracting multiple visual features of the object of interest, and according to the visual features, retrieving related advertising information in an advertisement database. Through the method and the system provided in embodiments of the present invention, a push advertisement may be closely relevant to the content of the video, thereby meeting personalized requirements of a user to a certain extent and achieving personalized advertisement push.04-18-2013
20130129197IMAGE RESTORATION BY VECTOR QUANTIZATION UTILIZING VISUAL PATTERNS - The restoration of images by vector quantization utilizing visual patterns is disclosed. One disclosed embodiment comprises restoring detail in a transition region of an unrestored image, by first identifying the transition region and forming blurred visual pattern blocks. These blurred visual pattern blocks are compared to a pre-trained codebook, and a corresponding high-quality visual pattern blocks is obtained. The high-quality visual pattern block is then blended with the unrestored image to form a restored image.05-23-2013
20130129196Image Adjustment - Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.05-23-2013
20080199072IMAGE PROCESSING DEVICE AND METHOD, LEARNING DEVICE AND METHOD, RECORDING MEDIUM, AND PROGRAM - With the present invention, data continuity is used at the time of converting an input image into high-quality image data with higher quality than the input image data, to obtain processing results which are more accurate and have higher precision. A class tap extracting unit (08-21-2008
20100290699Landmarks 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.11-18-2010
20100316283METHOD FOR EXTRACTING SPATIAL KNOWLEDGE FROM AN INPUT SIGNAL USING COMPUTATIONAL MANIFOLDS - A processor architecture for a learning machine is presented which uses a massive array of processing elements having local, recurrent connections to form global associations between functions defined on manifolds. Associations between these functions provide the basis for learning cause-and-effect relationships involving vision, audition, tactile sensation and kinetic motion. Two arbitrary input signals hold each other in place in a manifold association processor and form the basis of short-term memory.12-16-2010
20120020550IMAGE PROCESSING APPARATUS AND METHOD, AND PROGRAM - The present disclosure provides an image processing apparatus, including: a recognition section adapted to recognize, based on a learning result obtained by learning of a learning image regarding a predetermined object, the object in a predetermined frame of an input image formed from a plurality of frames which are continuous in time; and a setting section adapted to set a parameter to be used for a process to be carried out for a later frame which is later in time than the predetermined frame of the input image in response to a difference in image information between an object image, which is an image in a region of the object recognized in the predetermined frame, and the learning image; the recognition section recognizing the object in the later frame for which the process is carried out based on the parameter set by the setting section.01-26-2012

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