| Class / Patent application number | Description | Number of patent applications / Date published |
|---|
| 382160000 | Generating a standard by statistical analysis | 47 |
| 20090092313 | INFORMATION PROCESSING APPARATUS AND METHOD, PROGRAM, AND RECORDING MEDIUM - An information processing apparatus includes a statistical analysis processing device configured to perform statistical analysis processing, an acquisition device configured to acquire samples to be processed by the statistical analysis processing device, a classification device configured to classify the samples acquired by the acquisition device, and a selection device configured to select from the samples classified by the classification device learning samples to be used in the statistical analysis processing by the statistical analysis processing device. | 04-09-2009 |
| 20090116737 | Machine Learning For Tissue Labeling Segmentation - A method for directed machine learning includes receiving features including intensity data and location data of an image, condensing the intensity data and the location data into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image. | 05-07-2009 |
| 20090136123 | Program pattern analyzing apparatus, pattern appearance status information production method, pattern information generating apparatus, and program - Conventionally, there is the problem that a source program that is to be converted cannot be properly analyzed and the conversion ratio cannot be improved. The present invention provides a program pattern analyzing apparatus, comprising: a pattern information storage portion | 05-28-2009 |
| 20090154797 | APPARATUS AND METHOD FOR STEGANALYSIS - An apparatus and method for steganalysis that enhances the ability to detect distortion introduced by data hiding. In embodiments of the invention, a pixel grayscale value in an image is predicted by using its neighboring grayscale values of neighboring pixels. Further, a prediction-error image is produced by subtracting the image from its predicted image. The prediction-error image may is employed to remove at least some variations in image data other than those associated with data hiding an thus, at least partially offsets variations from image aspects other than data hiding. | 06-18-2009 |
| 20090161948 | COEFFICIENT LEARNING APPARATUS, COEFFICIENT LEARNING METHOD, IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD AND PROGRAMS - A coefficient learning apparatus includes: a student-image generation section configured to generate a student image from the teacher image; a class classification section configured to sequentially set each of pixels in the teacher image as a pixel of interest and generate a class for the pixel of interest from the values of a plurality of specific pixels; a weight computation section configured to add up feature quantities; and a processing-coefficient generation section configured to generate a prediction coefficient on the basis of a determinant including said deterioration equation and a weighted constraint condition equation. | 06-25-2009 |
| 20090202145 | LEARNING APPARTUS, LEARNING METHOD, RECOGNITION APPARATUS, RECOGNITION METHOD, AND PROGRAM - A learning apparatus includes: first feature quantity calculating means for pairing a predetermined pixel and a different pixel in each of a plurality of learning images, which includes a learning image containing a target object to be recognized and a learning image not containing the target object, and calculating a first feature quantity of the pair by calculating a texture distance between an area including the predetermined pixel and an area including the different pixel; and first discriminator generating means for generating a first discriminator for detecting the target object from an image by a statistical learning using a plurality of the first feature quantities. | 08-13-2009 |
| 20090297021 | Optical 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. | 12-03-2009 |
| 20100142803 | Transductive Multi-Label Learning For Video Concept Detection - This disclosure describes various exemplary method and computer program products for transductive multi-label classification in detecting video concepts for information retrieval. This disclosure describes utilizing a hidden Markov random field formulation to detect labels for concepts in a video content and modeling a multi-label interdependence between the labels by a pairwise Markov random field. The process groups the labels into several parts to speed up a labeling inference and calculates a conditional probability score for the labels, the conditional probability scores are ordered for ranking in a video retrieval evaluation. | 06-10-2010 |
| 20100177957 | Object detecting device, learning device, object detecting method, and program - An object detecting device includes a comparing unit to extract feature amounts for two regions on a determining object image and compare a feature amount based on the two feature amounts extracted; and a computing unit to select one of two values having different absolute values according to the comparison result, and compute an evaluation value to determine whether or not an object is included in the determining object image, by performing computation with the selected value. | 07-15-2010 |
| 20110019908 | Multi-Class Poisson Disk Sampling - A multi-class sampling component (MCSC) is described for selecting samples associated with two or more sampling classes to produce output information. The overall set of samples in the output information exhibits a desirable Poisson distribution. Further, each subset of samples associated with each respective class exhibits a Poisson distribution. The MCSC selects samples based on intra-class radius information (describing the minimum allowed distances between same-class samples) and inter-class radius information (describing the minimum allowed distances between different-class samples). The MCSC can be applied to different applications, such as an object placement application, a color stippling application, a sensor design application, and so on. | 01-27-2011 |
| 20110019909 | DEVICE AND METHOD FOR DETECTING WHETHER AN IMAGE IS BLURRED - The present invention is directed to a method for detecting or predicting ( | 01-27-2011 |
| 20110075920 | Multi-Level Contextual Learning of Data - Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure. | 03-31-2011 |
| 20110081074 | Method of Computing Global-to-Local Metrics for Recognition - A method of computing global-to-local metrics for recognition. Based on training examples with feature representations, the method automatically computes a local metric that varies over the space of feature representations to optimize discrimination and the performance of recognition systems. | 04-07-2011 |
| 20110135192 | LEARNING DEVICE AND METHOD, RECOGNITION DEVICE AND METHOD, AND PROGRAM - A learning device includes: a generating unit configured to generate an image having different resolution from an input image; an extracting unit configured to extract a feature point serving as a processing object from an image generated by the generating unit; a calculating unit configured to calculate the feature amount of the feature point by subjecting the feature point to filter processing employing a predetermined filter; and an identifier generating unit configured to generate an identifier for detecting a predetermined target object from the image by statistical learning employing the feature amount; with the filter including a plurality of regions, and the calculating unit taking the difference value of difference within the regions as the feature amount. | 06-09-2011 |
| 20120141021 | METHODS AND SYSTEMS FOR DATA ANALYSIS AND FEATURE RECOGNITION - Systems and methods for automated pattern recognition and object detection. The method can be rapidly developed and improved using a minimal number of algorithms for the data content to fully discriminate details in the data, while reducing the need for human analysis. The system includes a data analysis system that recognizes patterns and detects objects in data without requiring adaptation of the system to a particular application, environment, or data content. The system evaluates the data in its native form independent of the form of presentation or the form of the post-processed data. | 06-07-2012 |
| 20120263376 | SUPERVISED AND SEMI-SUPERVISED ONLINE BOOSTING ALGORITHM IN MACHINE LEARNING FRAMEWORK - A method for classification of samples comprising providing a trained statistical model based upon a set of initial samples. Receiving a set of first samples and training a first statistical model base upon the first set of samples, where the first statistical model is of the same class as the trained statistical model. Receiving a set of second samples and training a second statistical model base upon the second set of samples, where the second statistical model is of the same class as the trained statistical model. The trained statistical model, the first statistical model, and the second statistical model, being independent of each other and collectively used to classify another sample. | 10-18-2012 |
| 20130114889 | HEAD DETECTING METHOD, HEAD DETECTING APPARATUS, ATTRIBUTE DETERMINING METHOD, ATTRIBUTE DETERMINING APPARATUS, PROGRAM, RECORDING MEDIUM, AND ATTRIBUTE DETERMINING SYSTEM - The present invention is to provide a head detecting method for detecting the head in an image correctly at high speed. | 05-09-2013 |
| 20130129201 | Method for Pan-Sharpening Panchromatic and Multispectral Images Using Wavelet Dictionaries - A method Pan-sharpens a single panchromatic (Pan) image and a single multispectral (MS) image. A wavelet transform is applied to the Pan image and the MS image to obtain a wavelet transformed Pan image and a wavelet transformed MS image. Features, in the form of vectors, are extracted from the wavelet transformed Pan image and the wavelet transformed MS image. The features are separated into features without missing values and features with missing values. A dictionary is learned from features without missing values and used to predict the values for the features with the missing values. After the predicting, the features of the low frequency wavelet coefficients and the high frequency coefficients to form a fused wavelet coefficient map, and an inverse wavelet transform is applied to the fused wavelet coefficient map to obtain a fused MS image. | 05-23-2013 |
| 20130129202 | LARGE-SCALE STRONGLY SUPERVISED ENSEMBLE METRIC LEARNING - Systems and methods for metric learning include iteratively determining feature groups of images based on its derivative norm. Corresponding metrics of the feature groups are learned by gradient descent based on an expected loss. The corresponding metrics are combined to provide an intermediate metric matrix as a sparse representation of the images. A loss function of all metric parameters corresponding to features of the intermediate metric matrix are optimized, using a processor, to learn a final metric matrix. Eigenvalues of the final metric matrix are projected onto a simplex. | 05-23-2013 |
| 20130177238 | DEVICE AND METHOD FOR INTERNALLY AND EXTERNALLY ASSESSING WHITELISTS - A white list inside or outside determining apparatus includes: a first feature data extracting unit which extracts first feature data from an image by using a first transformation formula created based on preliminary learning images; a second feature data extracting unit which extracts second feature data from an image by using a second transformation formula created from the preliminary learning images and application learning images; a first matching unit which performs matching between a registration image and a collation image by using the first transformation formula; and a second matching unit which performs matching between a registration image and a collation image by using the second transformation formula. Weights of a matching result of the first matching unit and a matching result of the second matching unit are changed according to the number of preliminary learning images and the number of application learning images. | 07-11-2013 |
| 20130202200 | COMPUTER-AIDED ASSIGNMENT OF RATINGS TO DIGITAL SAMPLES OF A MANUFACTURED WEB PRODUCT - A computerized rating tool is described that assists a user in efficiently and consistently assigning expert ratings (i.e., labels) to a large collection of training images representing samples of a given product. The rating tool provides mechanisms for visualizing the training images in an intuitive and configurable fashion, including clustering and ordering the training images. In some embodiments, the rating tool provides an easy-to-use interface for exploring multiple types of defects represented in the data and efficiently assigning expert ratings. In other embodiments, the computer automatically assigns ratings (i.e., labels) to the individual clusters containing the large collection of digital images representing the samples. In addition, the computerized tool has capabilities ideal for labeling very large datasets, including the ability to automatically identify and select a most relevant subset of the images for a defect and to automatically propagate labels from this subset to the remaining images without requiring further user interaction. | 08-08-2013 |
| 20130301911 | APPARATUS AND METHOD FOR DETECTING BODY PARTS - Provided is an apparatus and method for detecting body parts, the method including identifying a group of sub-images relevant to a body part in an image to be detected, assigning a reliability coefficient for the body part to the sub-images in the group of sub-images based on a basic vision feature of the sub-images and an extension feature of the sub-images to neighboring regions, and detecting a location of the body part by overlaying sub-images having reliability coefficients higher than a threshold value. | 11-14-2013 |
| 20140072209 | IMAGE FUSION USING SPARSE OVERCOMPLETE FEATURE DICTIONARIES - Approaches for deciding what individuals in a population of visual system “neurons” are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm. | 03-13-2014 |
| 20140133745 | OBJECT RECOGNITION DEVICE - A learning unit | 05-15-2014 |
| 20140205189 | Techniques for Ground-Level Photo Geolocation Using Digital Elevation - Techniques for generating cross-modality semantic classifiers and using those cross-modality semantic classifiers for ground level photo geo-location using digital elevation are provided. In one aspect, a method for generating cross-modality semantic classifiers is provided. The method includes the steps of: (a) using Geographic Information Service (GIS) data to label satellite images; (b) using the satellite images labeled with the GIS data as training data to generate semantic classifiers for a satellite modality; (c) using the GIS data to label Global Positioning System (GPS) tagged ground level photos; (d) using the GPS tagged ground level photos labeled with the GIS data as training data to generate semantic classifiers for a ground level photo modality, wherein the semantic classifiers for the satellite modality and the ground level photo modality are the cross-modality semantic classifiers. | 07-24-2014 |
| 20140241623 | Window Dependent Feature Regions and Strict Spatial Layout for Object Detection - Systems and methods for object detection by receiving an image; segmenting the image and identifying candidate bounding boxes which may contain an object; for each candidate bounding box, dividing the box into overlapped small patches, and extracting dense features from the patches; during a training phase, applying a learning process to learn one or more discriminative classification models to classify negative boxes and positive boxes; and during an operational phase, for a new box generated from the image, applying the learned classification model to classify whether the box contains an object. | 08-28-2014 |
| 20140270495 | Multiple Cluster Instance Learning for Image Classification - The techniques and systems described herein create and train a multiple clustered instance learning (MCIL) model based on image features and patterns extracted from training images. The techniques and systems separate each of the training images into a plurality of instances (or patches), and then learn multiple instance-level classifiers based on the extracted image features. The instance-level classifiers are then integrated into the MCIL model so that the MCIL model, when applied to unclassified images, can perform image-level classification, patch-level clustering, and pixel-level segmentation. | 09-18-2014 |
| 20140270496 | DISCRIMINATIVE DISTANCE WEIGHTING FOR CONTENT-BASED RETRIEVAL OF DIGITAL PATHOLOGY IMAGES - Content-based retrieval of digital pathology images (DPI) is a fundamental component in an intelligent DPI processing and management system. The fundamental procedure of the retrieval is evaluating the similarity between the query image and every image in the database with some distance function, and sorting of the latter based on their distances to the query. A novel approach to optimally combine a set of existing distance functions into a stronger distance that is suitable for retrieving DPI in a way respecting human perception of image similarity is described herein. | 09-18-2014 |
| 20140321738 | DICTIONARY CREATION DEVICE, IMAGE PROCESSING DEVICE, IMAGE PROCESSING SYSTEM, DICTIONARY CREATION METHOD, IMAGE PROCESSING METHOD, AND PROGRAM - A dictionary creation device including a blurred image generation unit which outputs a blurred image generated by performing a blurring process to a learning image together with a blur parameter indicating a blurring state of the blurred image, a patch pair generation unit which generates a restoration patch and a blurred patch as a patch pair that is composed of the patches located at the corresponding positions of the learning image and the blurred image, and a registration unit which associates the patch pair with a blur parameter corresponding to the blurred patch in the patch pair and registers them in a dictionary. | 10-30-2014 |
| 20140328537 | Automatic Learning Method for the Automatic Learning of Forms of Appearance of Objects in Images - An automatic learning method for the automatic learning of the forms of appearance of objects in images in the form of object features ( | 11-06-2014 |
| 20140334721 | METHODS AND APPARATUS FOR CAPTURING, PROCESSING, TRAINING, AND DETECTING PATTERNS USING PATTERN RECOGNITION CLASSIFIERS - A system, methods, and apparatus for generating pattern recognition classifiers are disclosed. An example method includes identifying graphical objects within an image of a card object, for each identified graphical object: i) creating a bounding region encompassing the graphical object such that a border of the bounding region is located at a predetermined distance from segments of the graphical object, ii) determining pixels within the bounding region that correspond to the graphical object, iii) determining an origin of the graphical object based on an origin rule, iv) determining a text coordinate relative to the origin for each determined pixel, and v) determining a statistical probability that features are present within the graphical object, each of the features including at least one pixel having text coordinates and for each graphical object type, combining the statistical probabilities for each of the features of the identified graphical objects into a classifier data structure. | 11-13-2014 |
| 20140341465 | REAL-TIME POSE ESTIMATION SYSTEM USING INERTIAL AND FEATURE MEASUREMENTS - A hybrid estimator system using visual and inertial sensors for real-time pose tracking on devices with limited processing power using at least one processor, a memory, a storage and communications through a protocol and one or more than one software module for a hybrid estimator, real-time algorithm selection to process different measurements, statistical learning for these characteristics to compute the expected device computing cost of any strategy for allocating measurements to algorithms, and algorithm selection based on the statistical learning module. | 11-20-2014 |
| 20150023590 | METHOD AND SYSTEM FOR HUMAN ACTION RECOGNITION - A method and a system for human action recognition are provided. In the method, a plurality of training data corresponding to a plurality of gestures are received and clustered into at least one group according to similarity between the training data, where the training data represent the gestures, and a corresponding relationship between the training data and the gestures may be one-to-one or many-to-one. An image sequence of human action is captured, and a data representing the human action to be identified is obtained there from. Then, a specific group having the highest similarity with the data to be identified is selected from the groups, and a ranking result of all the training data within the specific group is obtained through a rank classifier and the data to be identified. Finally, the human action is identified as the gesture represented by the first training data in the ranking result. | 01-22-2015 |
| 20150117766 | CLASS DISCRIMINATIVE FEATURE TRANSFORMATION - A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors. | 04-30-2015 |
| 20150117767 | METHOD AND APPARATUS OF DETERMINING AIR QUALITY - The present invention discloses a method and apparatus of determining air quality. The method comprising: determining at least one key area; acquiring a reference clear image, a training image under poor air quality and corresponding actual air quality index in at least one location of the key area; and training an air quality model of the key area based on feature extracted from the reference clear image and the training image and based on the actual air quality index. With the method and apparatus of the invention, air quality can be determined based on image. | 04-30-2015 |
| 20150294191 | SYSTEM AND METHOD FOR PREDICTING ICONICITY OF AN IMAGE - A system and method for evaluating iconicity of an image are provided. In the method, at least one test image is received, each test image including an object in a selected class. Properties related to iconicity are computed for each test image. The properties may include one or more of: a) a direct measure of iconicity, which is computed with a direct iconicity prediction model which has been learned on a set of training images, each training image labeled with an iconicity score; b) one or more class-independent properties; and c) one or more class-dependent properties. A measure of iconicity of each of the test images is computed, based on the computed properties. By combining a set of complementary properties, an iconicity measure which shows good agreement with human evaluations of iconicity can be obtained. | 10-15-2015 |
| 20150371112 | BUILDING MATERIAL CLASSIFICATIONS FROM IMAGERY - Imagery is used to identify architectural elements that have known architectural patterns. Feature sets associated with a surface and architectural elements in a building model image are compared with known architectural standards of materials to determine the surface building materials and architectural details of a textured building model. In addition, specific texture patterns can assist final material selections for a repair/replacement. | 12-24-2015 |
| 20160012314 | ENSEMBLE SPARSE MODELS FOR IMAGE ANALYSIS AND RESTORATION | 01-14-2016 |
| 20160026861 | METHODS AND APPARATUS FOR CAPTURING, PROCESSING, TRAINING, AND DETECTING PATTERNS USING PATTERN RECOGNITION CLASSIFIERS - A system, methods, and apparatus for generating pattern recognition classifiers are disclosed. An example method includes identifying graphical objects within an image of a card object, for each identified graphical object: i) creating a bounding region encompassing the graphical object such that a border of the bounding region is located at a predetermined distance from segments of the graphical object, ii) determining pixels within the bounding region that correspond to the graphical object, iii) determining an origin of the graphical object based on an origin rule, iv) determining a text coordinate relative to the origin for each determined pixel, and v) determining a statistical probability that features arc present within the graphical object, each of the features including at least one pixel having text coordinates and for each graphical object type, combining the statistical probabilities for each of the features of the identified graphical objects into a classifier data structure. | 01-28-2016 |
| 20160070987 | IDENTIFICATION APPARATUS AND METHOD FOR CONTROLLING IDENTIFICATION APPARATUS - An identification apparatus performs classification using a plurality of classifiers, and calculates the reliability of its classification result. A data obtaining unit obtains input data. A feature quantity obtaining unit obtains a feature quantity corresponding to the input data. A plurality of classifiers receive input of the feature quantity and perform classification based on the input feature quantity. An identification unit inputs the feature quantity into each of the classifiers, and generates a single second classification result based on a plurality of classification results obtained from the classifiers. A reliability generation unit generates a reliability of the second classification result based on variations across the plurality of classification results. | 03-10-2016 |
| 20160098618 | 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 comprising a set of global features. The method includes extracting a set of global features for the image. The method further includes encoding the extracted set of global features into a high-dimensional feature vector. The method further includes reducing the dimension of the high-dimensional feature vector. The method further includes applying a machine-learned model to assign an aesthetic score to the image, wherein a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score. | 04-07-2016 |
| 20160110630 | IMAGE BASED OBJECT CLASSIFICATION - A method for classifying an object in image data to one out of a set of classes using a classifier, said image data comprising an image of the object, each class indicating a property common to a group of objects, the method comprising the steps of obtaining said classifier used to estimate for an input feature vector a probability for each of the set of classes, one probability indicating whether the input feature vector belongs to one class; extracting a feature vector from said image data; using the obtained classifier to estimate the probabilities for the extracted feature vector; and evaluating the estimated probabilities for determining whether the object does not belong to any one of the set of classes based using a quality indicator. | 04-21-2016 |
| 20160125274 | DISCOVERING VISUAL CONCEPTS FROM WEAKLY LABELED IMAGE COLLECTIONS - Images uploaded to photo sharing websites often include some tags or sentence descriptions. In an example embodiment, these tags or descriptions, which might be relevant to the image contents, become the weak labels of these images. The weak labels can be used to identify concepts for the images using an iterative hard instance learning algorithm to discover visual concepts from the label and visual feature representations in the weakly labeled images. The visual concept detectors can be directly applied to concept recognition and detection. | 05-05-2016 |
| 20160155022 | SEMI-SUPERVISED METHOD FOR TRAINING MULTIPLE PATTERN RECOGNITION AND REGISTRATION TOOL MODELS | 06-02-2016 |
| 20160180199 | AUTOMATIC SURVEILLANCE VIDEO MATTING USING A SHAPE PRIOR | 06-23-2016 |
| 20160189000 | SCALABLE IMAGE MATCHING - Various embodiments may increase scalability of image representations stored in a database for use in image matching and retrieval. For example, a system providing image matching can obtain images of a number of inventory items, extract features from each image using a feature extraction algorithm, and transform the same into their feature descriptor representations. These feature descriptor representations can be subsequently stored and used to compare against query images submitted by users. Though the size of each feature descriptor representation isn't particularly large, the total number of these descriptors requires a substantial amount of storage space. Accordingly, feature descriptor representations are compressed to minimize storage and, in one example, machine learning can be used to compensate for information lost as a result of the compression. | 06-30-2016 |
| 20180025231 | SYSTEM AND METHOD FOR PROVIDING SURVEILLANCE DATA | 01-25-2018 |
