Entries |
Document | Title | Date |
20080260239 | OBJECT IMAGE DETECTION METHOD - The present invention discloses an object image detection method, which uses a coarse-to-fine strategy to detect objects. The method of the present invention comprises steps: acquiring an image and pre-processing the image to achieve dimensional reduction and information fusion; using a trained filter to screen features; and sequentially using a coarse-level MLP verifier and a fine-level MLP verifier to perform a neural network image detection to determine whether the features of the image match the features of the image of a target object. The present invention simultaneously uses three mainstream image detection methods, including the statistic method, neural network method and adaboost method, to perform image detection. Therefore, the present invention has the advantages of the rapidity of the adaboost method and the accuracy of the neural network method at the same time. | 10-23-2008 |
20080310709 | Annotating Video Segments Using Feature Rhythm Models - Each video segment in a plurality of video segments is annotated with an indicator of the likelihood that the respective video segment shows a particular feature. The plurality of video segments forms an episode of interest from a given video domain. Initial feature probabilities are calculated for respective ones of the plurality of video segments using a machine learning algorithm. Each initial feature probability indicates the likelihood that its respective video segment shows the particular feature. Refined feature probabilities are determined for respective ones of the plurality of video segments by finding the most probable state sequence in a finite state machine. This is accomplished at least in part using the determined initial feature probabilities. Finally, each of the video segments in the plurality of vides segments is annotated with its respective refined feature probability. | 12-18-2008 |
20090080768 | RECOGNITION METHOD FOR IMAGES BY PROBING ALIMENTARY CANALS - The present invention relates to a recognition method for images by probing alimentary canals. First, series first image data is received. Then, according to a plurality of judgments, judge if the first image data exceeds a threshold value. If so, the image data is stored and second image data is inputted for recognition. Thereby, by the plurality of judgments with partially identical characteristics, multiple diseases can be recognized at a time, and repeated operation can be eliminated and the processing time be reduced. In addition, by integrating different recognition methods, the amount of system operation can be reduced, and the operation speed can be thereby improved. | 03-26-2009 |
20090202144 | MUSIC SCORE DECONSTRUCTION - Data set generation and data set presentation for image processing are described. The processing determines a location for each of one or more musical artifacts in the image and identifies a corresponding label for each of the musical artifacts, generating a training file that associates the identified labels and determined locations of the musical artifacts with the image, and presenting the training file to a neural network for training. | 08-13-2009 |
20090304267 | IDENTIFICATION OF ITEMS DEPICTED IN IMAGES - In an example embodiment, a method of identifying an item depicted in an image is provided. In this method, the image depicting the item is accessed; in addition, other images and their item identifiers are also accessed. A match of the image with one of the other images is identified. With a match, the image is then associated with an item identifier of the matched image. | 12-10-2009 |
20100040281 | Systems and Methods Employing Cooperative Optimization-Based Dimensionality Reduction - Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used. | 02-18-2010 |
20100135574 | Image processing using neural network - Image processing method wherein each image is composed of an array of image points, so called pixels or voxels particularly in a two-, three-, or more dimensional space respectively each image point being univocally defined by its position within the array of image points and by one or more numerical parameters defining the image point appearance as regards characteristics of brightness, grey, colour shade or the like, and wherein each image point is considered to be a node of an artificial neural network, the image being processed as a function of parameters defining the appearance of each pixel as values of the nodes of said artificial neural network and as a function of connections of each pixel under processing with neighbouring pixels composed of pixels of a predetermined subset of pixels, particularly with neighbouring pixels of said pixel under processing, so called pixel window, while pixels of the new image i.e. of the processed image are obtained by iterative evolution steps of parameters defining the appearance such as evolution steps of the value of nodes or by iterative evolution steps of values of the set of connections or by a combination of said evolutions, wherein the processing occurs by evolution iterative steps where each step is a function also of connections of neighbouring pixels with the pixel under examination, when each of said neighbouring pixels of the pixel under examination is considered also as a neighbouring pixel of one ore more or all pixels adjacent to said neighbouring pixel, which function is an immediate feedback contribution for determining appearance values of all other pixels. | 06-03-2010 |
20100166297 | Method for constructing prototype vectors in real time on the basis of input data of a neural process - The technical field of the invention is that of processing or generating image data, and it more particularly relates to a method for processing images consisting of pixels generated by an image sensor with a view to supplying input data to a simulated or wired neural process. The method is characterized in that it comprises a step of reading pixel-by-pixel in real time by processing means and a step of constructing prototype vectors during the pixel-by-pixel reading process on the basis of the values read, the prototype vectors constituting the input data of the neural process. It is intended for applications in global environmental perception and movement analysis. The method to which the invention relates may also be used for conventional image processing operations such as temporal filtering, and it makes it possible to save on computing time and memory space. | 07-01-2010 |
20100183217 | METHOD AND APPARATUS FOR IMAGE PROCESSING - Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos. | 07-22-2010 |
20100215253 | CALCULATION PROCESSING APPARATUS AND CONTROL METHOD THEREOF - A calculation processing apparatus, which executes calculation processing based on a network composed by hierarchically connecting a plurality of processing nodes, assigns a partial area of a memory to each of the plurality of processing nodes, stores a calculation result of a processing node in a storable area of the partial area assigned to that processing node, and sets, as storable areas, areas that store the calculation results whose reference by all processing nodes connected to the subsequent stage of that processing node is complete. The apparatus determines, based on the storage states of calculation results in partial areas of the memory assigned to the processing node designated to execute the calculation processing of the processing nodes, and to processing nodes connected to the previous stage of the designated processing node, whether or not to execute a calculation of the designated processing node. | 08-26-2010 |
20100246939 | Image Processing Apparatus and Method, Learning Apparatus and Method, and Program - The present invention relates to an image processing apparatus and method, a learning apparatus and method, and a program which allow reliable evaluation of whether or not the subject appears sharp. | 09-30-2010 |
20100278420 | Predicate Logic based Image Grammars for Complex Visual Pattern Recognition - First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces. | 11-04-2010 |
20110007963 | COMBINER FOR IMPROVING HANDWRITING RECOGNITION - Various technologies and techniques are disclosed that improve handwriting recognition operations. Handwritten input is received in training mode and run through several base recognizers to generate several alternate lists. The alternate lists are unioned together into a combined alternate list. If the correct result is in the combined list, each correct/incorrect alternate pair is used to generate training patterns. The weights associated with the alternate pairs are stored. At runtime, the combined alternate list is generated just as training time. The trained comparator-net can be used to compare any two alternates in the combined list. A template matching base recognizer is used with one or more neural network base recognizers to improve recognition operations. The system provides comparator-net and reorder-net processes trained on print and cursive data, and ones that have been trained on cursive-only data. The respective comparator-net and reorder-net processes are used accordingly. | 01-13-2011 |
20120045118 | IMAGE RESIZING FOR WEB-BASED IMAGE SEARCH - Image resizing for web-based searching is described. In one implementation, a system resizes a user-selected thumbnail image into a larger version of the image that emulates the quality of a large, original image, but without downloading the original image. First, the system extracts resizing parameters when each thumbnail image is created. Then, the system creates a codebook of primitive visual elements extracted from a collection of training images. The primitive visual elements in the codebook provide universal visual parts for reconstructing images. The codebook and a resizing plug-in can be sent once to the user over a background channel. When the user selects a thumbnail image for enlargement, the system resizes the thumbnail image via interpolation and then refines the enlarged image with primitive visual elements from the codebook. The refinement creates an enlarged image that emulates the quality of the large, original image, without downloading the original image. | 02-23-2012 |
20120121169 | METHOD AND APPARATUS FOR GENERATING SPECIAL-PURPOSE IMAGE ANALYSIS ALGORITHMS - Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images. | 05-17-2012 |
20120275690 | DISTRIBUTED ARTIFICIAL INTELLIGENCE SERVICES ON A CELL PHONE - A cell phone having distributed artificial intelligence services is provided. The cell phone includes a neural network for performing a first pass of object recognition on an image to identify objects of interest therein based on one or more criterion. The cell phone also includes a patch generator for deriving patches from the objects of interest. Each of the patches includes a portion of a respective one of the objects of interest. The cell phone additionally includes a transmitter for transmitting the patches to a server for further processing in place of an entirety of the image to reduce network traffic. | 11-01-2012 |
20130011050 | Filter Setup Learning for Binary Sensor - The invention relates to forming an image using binary pixels. Binary pixels are pixels that have only two states, a white state when the pixel is exposed and a black state when the pixel is not exposed. The binary pixels have color filters on top of them, and the setup of color filters may be initially unknown. A setup making use of a statistical approach may be used to determine the color filter setup to produce correct output images. Subsequently, the color filter information may be used with the binary pixel array to produce images from the input images that the binary pixel array records. | 01-10-2013 |
20130034298 | IMAGE-BASED CRACK DETECTION - Contact-less remote-sensing crack detection and/quantification methodologies are described, which are based on three-dimensional (3D) scene reconstruction, image processing, and pattern recognition. The systems and methodologies can utilize depth perception for detecting and/or quantifying cracks. These methodologies can provide the ability to analyze images captured from any distance and using any focal length or resolution. This adaptive feature may be especially useful for incorporation into mobile systems, such as unmanned aerial vehicles (UAV) or mobile autonomous or semi-autonomous robotic systems such as wheel-based or track-based radio controlled robots, as utilizing such structural inspection methods onto those mobile platforms may allow inaccessible regions to be properly inspected for cracks. | 02-07-2013 |
20130163858 | COMPONENT RECOGNIZING APPARATUS AND COMPONENT RECOGNIZING METHOD - Disclosed are a component recognizing apparatus and a component recognizing method. The component recognizing apparatus includes: an image preprocessing unit configured to extract component edges from an input component image by using a plurality of edge detecting techniques, and detect a component region by using the extracted component edges; a feature extracting unit configured to extract a component feature from the detected component region, and create a feature vector by using the component feature; and a component recognizing unit configured to input the created feature vector to an artificial neural network which has learned in advance to recognize a component category through a plurality of component image samples, and recognize the component category according to a result. | 06-27-2013 |
20130188863 | Method for context aware text recognition - A method for context-aware text recognition employing two neuromorphic computing models, auto-associative neural network and cogent confabulation. The neural network model performs the character recognition from input image and produces one or more candidates for each character in the text image input. The confabulation models perform the context-aware text extraction and completion, based on the character recognition outputs and the word and sentence knowledge bases. | 07-25-2013 |
20130216126 | USER EMOTION DETECTION METHOD AND ASSOCIATED HANDWRITING INPUT ELECTRONIC DEVICE - A user emotion detection method for a handwriting input electronic device is provided. The method includes steps of: obtaining at least one handwriting input characteristic parameter; determining a user emotion parameter by an artificial neural network of the handwriting input electronic device according to the handwriting input characteristic value and at least one associated linkage value; displaying the user emotion parameter on a touch display panel of the handwriting input electronic device; receiving a user feedback parameter; determining whether to adjust the at least one associated linkage value and if yes, adjusting the at least one associated linkage value according to the user feedback parameter to construct and adjust the artificial neural network. | 08-22-2013 |
20130266214 | TRAINING AN IMAGE PROCESSING NEURAL NETWORK WITHOUT HUMAN SELECTION OF FEATURES - A method for training an image processing neural network without human selection of features may include providing a training set of images labeled with two or more classifications, providing an image processing toolbox with image transforms that can be applied to the training set, generating a random set of feature extraction pipelines, where each feature extraction pipeline includes a sequence of image transforms randomly selected from the image processing toolbox and randomly selected control parameters associated with the sequence of image transforms. The method may also include coupling a first stage classifier to an output of each feature extraction pipeline and executing a genetic algorithm to conduct genetic modification of each feature extraction pipeline and train each first stage classifier on the training set, and coupling a second stage classifier to each of the first stage classifiers in order to increase classification accuracy. | 10-10-2013 |
20130343641 | SYSTEM AND METHOD FOR LABELLING AERIAL IMAGES - A system and method for labelling aerial images. A neural network generates predicted map data. The parameters of the neural network are trained by optimizing an objective function which compensates for noise in the map images. The function compensates both omission noise and registration noise. | 12-26-2013 |
20140016858 | SPIKING NEURON NETWORK SENSORY PROCESSING APPARATUS AND METHODS - Apparatus and methods for detecting salient features. In one implementation, an image processing apparatus utilizes latency coding and a spiking neuron network to encode image brightness into spike latency. The spike latency is compared to a saliency window in order to detect early responding neurons. Salient features of the image are associated with the early responding neurons. A dedicated inhibitory neuron receives salient feature indication and provides inhibitory signal to the remaining neurons within the network. The inhibition signal reduces probability of responses by the remaining neurons thereby facilitating salient feature detection within the image by the network. Salient feature detection can be used for example for image compression, background removal and content distribution. | 01-16-2014 |
20140086479 | SIGNAL PROCESSING APPARATUS, SIGNAL PROCESSING METHOD, OUTPUT APPARATUS, OUTPUT METHOD, AND PROGRAM - There is provided a signal processing apparatus including a learning unit that learns a plurality of base signals of which coefficients become sparse, using a cost function including a term showing a correspondence between the coefficients, such that signals are represented by a linear operation of the plurality of base signals. | 03-27-2014 |
20140086480 | SIGNAL PROCESSING APPARATUS, SIGNAL PROCESSING METHOD, OUTPUT APPARATUS, OUTPUT METHOD, AND PROGRAM - There is provided a signal processing apparatus including a learning unit that learns a plurality of base signals of which coefficients become sparse, for each of features of signals, such that the signals are represented by a linear operation of the plurality of base signals. | 03-27-2014 |
20140177946 | HUMAN DETECTION APPARATUS AND METHOD - Disclosed herein is an apparatus and method for detecting a person from an input video image with high reliability by using gradient-based feature vectors and a neural network. The human detection apparatus includes an image preprocessing unit for modeling a background image from an input image. A moving object area setting unit sets a moving object area in which motion is present by obtaining a difference between the input image and the background image. A human region detection unit extracts gradient-based feature vectors for a whole body and an upper body from the moving object area, and detects a human region in which a person is present by using the gradient-based feature vectors for the whole body and the upper body as input of a neural network classifier. A decision unit decides whether an object in the detected human region is a person or a non-person. | 06-26-2014 |
20140177947 | SYSTEM AND METHOD FOR GENERATING TRAINING CASES FOR IMAGE CLASSIFICATION - A system and method for generating training images. An existing training image is associated with a classification. The system includes an image processing module that performs color-space deformation on each pixel of the existing training image and then associates the classification to the color-space deformed training image. The technique may be applied to increase the size of a training set for training a neural network. | 06-26-2014 |
20140241616 | MATCHING USERS ACROSS IDENTIFIABLE SERVICES BASED ON IMAGES - A method for determining that a user associated with a first identifiable device or identifiable service is also associated with a second identifiable device or identifiable service by a) generating one or more first image descriptors for one or more first images stored on the first identifiable service associated with a first user, b) generating one or more second image descriptors for one or more second images stored on the second identifiable service associated with a second user, c) calculating, based on the generated first and second image descriptors, the probability that the first user is also the second user. Also provided is a computer readable storage medium containing program code for implementing the method. | 08-28-2014 |
20140363074 | MULTI-SCRIPT HANDWRITING RECOGNITION USING A UNIVERSAL RECOGNIZER - Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed. | 12-11-2014 |
20150036919 | SYSTEMS AND METHODS FOR IMAGE CLASSIFICATION BY CORRELATING CONTEXTUAL CUES WITH IMAGES - A sample set of images is received. Each image in the sample set may be associated with one or more social cues. Correlation of each image in the sample set with an image class is scored based on the one or more social cues associated with the image. Based on the scoring, a training set of images to train a classifier is determined from the sample set. In an embodiment, an extent to which an evaluation set of images correlates with the image class is determined. The determination may comprise ranking a top scoring subset of the evaluation set of images. | 02-05-2015 |
20150036920 | CONVOLUTIONAL-NEURAL-NETWORK-BASED CLASSIFIER AND CLASSIFYING METHOD AND TRAINING METHODS FOR THE SAME - The present invention relates to a convolutional-neural-network-based classifier, a classifying method by using a convolutional-neural-network-based classifier and a method for training the convolutional-neural-network-based classifier. The convolutional-neural-network-based classifier comprises: a plurality of feature map layers, at least one feature map in at least one of the plurality of feature map layers being divided into a plurality of regions; and a plurality of convolutional templates corresponding to the plurality of regions respectively, each of the convolutional templates being used for obtaining a response value of a neuron in the corresponding region. | 02-05-2015 |
20150063685 | IMAGE DISTORTION CORRECTION METHOD AND IMAGE DISTORTION CORRECTION DEVICE USING THE SAME - An image distortion correction method and an image distortion correction device are provided. The image distortion correction method uses a neural network model to perform a correcting operation on an original image so as to obtain a correction image with plural correction points. Firstly, a position coordinate of the correction point is inputted into the neural network model, so that a first direction coordinate correction amount is outputted from the neural network model. Then, the position coordinate of the correction point is inputted into the neural network model, so that a second direction coordinate correction amount is outputted from the neural network model. Afterwards, a pixel value of the original image corresponding to the first direction coordinate correction amount and the second direction coordinate correction amount is used as a pixel value of the correction point. | 03-05-2015 |
20150139536 | Image Classification Using Images with Separate Grayscale and Color Channels - Image classification techniques using images with separate grayscale and color channels are described. In one or more implementations, an image classification network includes grayscale filters and color filters which are separate from the grayscale filters. The grayscale filters are configured to extract grayscale features from a grayscale channel of an image, and the color filters are configured to extract color features from a color channel of the image. The extracted grayscale features and color features are used to identify an object in the image, and the image is classified based on the identified object. | 05-21-2015 |
20150139537 | METHODS AND APPARATUS FOR ESTIMATING ANGULAR MOVEMENT WITH A SINGLE TWO DIMENSIONAL DEVICE - Certain aspects of the present disclosure relate to methods and apparatus for neuro-simulation with a single two-dimensional device to track objects. The neuro-simulation may report a point of interest in an image that is provided by the device. The device may center on the point of interest using one or more actuators. The simulation mechanism may input pixels and output a plurality of angles to the actuators to adjust their direction. | 05-21-2015 |
20150347831 | DETECTION DEVICE, DETECTION PROGRAM, DETECTION METHOD, VEHICLE EQUIPPED WITH DETECTION DEVICE, PARAMETER CALCULATION DEVICE, PARAMETER CALCULATING PARAMETERS, PARAMETER CALCULATION PROGRAM, AND METHOD OF CALCULATING PARAMETERS - A detection device has a neural network process section performing a neural network process using parameters to calculate and output a classification result and a regression result of each of frames in an input image. The classification result shows a presence of a person in the input image. The regression result shows a position of the person in the input image. The parameters are determined based on a learning process using a plurality of positive samples and negative samples. The positive samples have segments of a sample image containing at least a part of the person and a true value of the position of the person in the sample image. The negative samples have segments of the sample image containing no person. | 12-03-2015 |
20160098844 | SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR SEARCHING AND SORTING IMAGES BY AESTHETIC QUALITY - A system, method, and computer program product for assigning an aesthetic score to an image. A method of the present invention includes receiving an image. The method further includes executing a neural network on the image to generate learned features. The method further includes applying a machine-learned model to assign an aesthetic score to the image, where a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score. The learned features are inputs to the machine-learned model. | 04-07-2016 |
20160104053 | Hierarchical Interlinked Multi-scale Convolutional Network for Image Parsing - A disclosed facial recognition system (and method) includes face parsing. In one approach, the face parsing is based on hierarchical interlinked multiscale convolutional neural network (HIM) to identify locations and/or footprints of components of a face image, The HIM generates multiple levels of image patches from different resolution images of the face image, where image patches for different levels have different resolutions. Moreover, the HIM integrates the image patches for different levels to generate interlinked image patches for different levels, where interlinked image patches for different levels have different resolutions. Furthermore, the HIM combines the interlinked image patches to identify refined locations and/or footprints of components. | 04-14-2016 |
20160104058 | GENERIC OBJECT DETECTION IN IMAGES - Neural networks for object detection in images are used with a spatial pyramid pooling (SPP) layer. Using the SPP network structure, a fixed-length representation is generated regardless of image size and scale. The feature maps are computed from the entire image once, and the features are pooled in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. Thus, repeated computation of the convolutional features is avoided while accuracy is enhanced. | 04-14-2016 |
20160117845 | CONTAMINATION LEVEL ESTIMATION METHOD FOR HIGH VOLTAGE INSULATORS - The contamination level estimation method for high voltage insulators collects samples of naturally contaminated insulators and builds an image data set for the collected insulators. Flashover voltages of several insulators samples are measured. ESDD levels of the collected insulators are estimated. Images are input to image processing algorithms to extract representative features. The images are segmented. Transforming the image from RGB color space into grayscale model excludes the background from the image. Subsequently, the segmented images are transferred back to RGB color space model using matrix manipulation. Since contaminants on the insulator surface affect the color of the insulator, the segmented image is transformed from RGB to HSV color space which is used for extracting statistical and linear algebraic features from the hue image. A trained artificial neural network correlates the extracted features to the contamination levels enabling testing of other contaminated insulators. | 04-28-2016 |
20160140424 | Object-centric Fine-grained Image Classification - Systems and methods are disclosed for classifying vehicles by performing scale aware detection; performing detection assisted sampling for convolutional neural network (CNN) training, and performing deep CNN fine-grained image classification to classify the vehicle type. | 05-19-2016 |
20160162777 | VISUAL CORTICAL CIRCUIT APPARATUS, VISUAL CORTICAL IMITATION SYSTEM AND OBJECT SEARCH SYSTEM USING VISUAL CORTICAL CIRCUIT APPARATUS - Provided us a visual cortical circuit apparatus comprising: a current mirror unit which uses a transistor as a current source to generate a current having the same size as that of a reaction; a transconductance unit which takes, as an input, the current generated by the current mirror unit and outputs a voltage using a transconductance; and a buffer unit for converting the voltage output from the transconductance unit into a current and buffering the current. | 06-09-2016 |
20160179844 | Neural Network Image Curation Control | 06-23-2016 |
20160196479 | IMAGE SIMILARITY AS A FUNCTION OF WEIGHTED DESCRIPTOR SIMILARITIES DERIVED FROM NEURAL NETWORKS | 07-07-2016 |
20160196672 | GRAPH IMAGE REPRESENTATION FROM CONVOLUTIONAL NEURAL NETWORKS | 07-07-2016 |
20160378195 | METHOD FOR RECOGNIZING HANDWRITING ON A PHYSICAL SURFACE - The invention relates to a method for recognizing handwriting on a physical surface on the basis of three-dimensional signals originating from sensors of a terminal, the method being characterized in that the signals are obtained on the basis of at least 3 different types of sensors, and in that it comprises steps of sampling, according to 3 axes and over a sliding time window, of inertial signals originating from the sensors, fusing the sampled signals into a 9-dimensional vector for each sampling period, converting the fused signals into a sequence of characteristic 9-dimensional vectors, and, when a signal characteristic of an input start has been detected, storing the sequence of characteristic vectors in a list of sequences of characteristic vectors, the preceding steps being repeated until the detection of a signal characteristic of an input end, the method furthermore comprising, on detection of said signal characteristic of an input end, a step of recognizing a word on the basis of the list of sequences of characteristic vectors created over the time window. | 12-29-2016 |
20160379091 | TRAINING A CLASSIFIER ALGORITHM USED FOR AUTOMATICALLY GENERATING TAGS TO BE APPLIED TO IMAGES - This disclosure relates to training a classifier algorithm that can be used for automatically selecting tags to be applied to a received image. For example, a computing device can group training images together based on the training images having similar tags. The computing device trains a classifier algorithm to identify the training images as semantically similar to one another based on the training images being grouped together. The trained classifier algorithm is used to determine that an input image is semantically similar to an example tagged image. A tag is generated for the input image using tag content from the example tagged image based on determining that the input image is semantically similar to the tagged image. | 12-29-2016 |
20170235996 | METHOD AND SYSTEM FOR COLLABORATIVE MULTI-SATELLITE REMOTE SENSING | 08-17-2017 |
20180025729 | Audio-Visual Speech Recognition with Scattering Operators | 01-25-2018 |
20190147299 | DATA PROCESSING METHOD AND APPARATUS FOR CONVOLUTIONAL NEURAL NETWORK | 05-16-2019 |
20190147302 | BILATERAL CONVOLUTION LAYER NETWORK FOR PROCESSING POINT CLOUDS | 05-16-2019 |
20190147341 | FULLY CONVOLUTIONAL INTEREST POINT DETECTION AND DESCRIPTION VIA HOMOGRAPHIC ADAPTATION | 05-16-2019 |
20190147582 | ADVERSARIAL LEARNING OF PHOTOREALISTIC POST-PROCESSING OF SIMULATION WITH PRIVILEGED INFORMATION | 05-16-2019 |
20220139066 | Scene-Driven Lighting Control for Gaming Systems - In one example, an electronic device may include a capturing unit to capture video content and audio content of an application being executed on the electronic device, an analyzing unit to analyze the video content and the audio content to generate a plurality of synthetic feature vectors, a processing unit to process the plurality of synthetic feature vectors to determine a content event corresponding to a scene displayed on the electronic device, and a controller to select an ambient effect profile corresponding to the content event and control a device according to the ambient effect profile to render an ambient effect in relation to the scene. | 05-05-2022 |
20220139074 | VERIFICATION OF EMBEDDED ARTIFICIAL NEURAL NETWORKS SYSTEMS AND METHODS - Various techniques are disclosed to provide for automated verification of the performance of embedded artificial neural networks (ANNs). In one example, a method includes converting a reference ANN to generate an embedded ANN for deployment on an imaging device. The method also includes deploying the reference ANN on a host. The method also includes processing predetermined images by the reference ANN on the host to generate host inference results. The method also includes receiving device inference results from the imaging device at the host in response to processing of the predetermined images by the embedded ANN on the imaging device. The method also includes comparing the device inference results with the host inference results to evaluate performance of the embedded ANN in relation to the reference ANN. Additional methods, devices, and systems are also provided. | 05-05-2022 |
20220139092 | MODEL GENERATING APPARATUS, METHOD AND PROGRAM, AND PREDICTION APPARATUS - A model generation apparatus according to one aspect of the present invention acquires a plurality of learning datasets each constituted by a first sample of a first time of predetermined data obtained in time series and feature information included in a second sample of the predetermined data of a future second time relative to the first time, and trains a prediction model, by machine learning, to predict feature information of the second time from the first sample of the first time, for each learning dataset. In the model generation apparatus, a rarity degree for is set each learning dataset, and, in the machine learning, the model generation apparatus trains more preponderantly on learning datasets having a higher rarity degree. | 05-05-2022 |