Abstract: According to one embodiment, an electronic apparatus includes a storage device which stores face thumbnail indexing information including face images and time stamp information, extracting module configure to assign time zones to a video content data and to extract face images belonging to each time zone based on a time stamp information, classifying module configure to classify facial images of the same person from the extracted facial images, calculating module configure to calculate a frequency of appearance of each classified facial image, and facial image indication module configure to display a list of the facial images included in the facial image indexing information in a facial image indication in a two-dimensional display area, the facial image indication having time-zone-specific display areas in columns corresponding to the time zones, each facial image displayed in each time-zone-specific display area being displayed in a size based on the frequency of appearance.

Abstract: Various embodiments of a system and methods for using contextual features to improve face recognition in digital images are described. A face recognition system may semi-automatically label faces and/or search for faces in images. Faces may be detected in the images and visual and non-visual features may be determined for each face. The determined features may be facial and/or contextual features. Features may also be determined from labels assigned to the faces. Pair-wise comparisons of the faces, using the determined features, may determine distances between pairs of faces. Some features may be used to determine the relevance of other features. Dependent on the calculated distances, similar faces may be grouped and suggested labels for faces may be provided. The system may be scalable, processing faces in groups to limit the use of system resources to a certain amount, regardless of the size of a digital image collection.

Abstract: Described is a technology for reducing the layout complexity of a graph, e.g., an STT graph. Nodes with similar incoming and outgoing links are grouped as candidate groupings to possibly be clustered. For each candidate grouping, if the nodes in the candidate grouping and/or the candidate grouping meet clustering criteria, the candidate grouping is clustered into a clustered node. The criteria may include user-specified parameters directed towards the nodes and/or the candidate groupings. For example, a node is removed from a group if the number of incoming links thereto or the number of outgoing links therefrom exceed maximum incoming and outgoing parameter values, respectively. A group is only clustered if the number of nodes therein meets a minimum size parameter.

Abstract: A method reduces multiplicative and additive noise in image pixels by clustering similar patches of the pixels into clusters. The clusters form nodes in an affinity net of nodes and vertices. From each cluster, a dictionary is learned by a sparse combination of corresponding atoms in the dictionaries. The patches are aggregated collaboratively using the dictionaries to construct a denoised image.

Abstract: An image database device which stores images shot by a lot of people who have freely participated to generate a photo-based virtual world. An image database device receives images shot by a plurality of users through communication over a network. The received image has a shooting position represented by common coordinates used among the users, which represent the position where the image is shot, its shooting conditions, and its image attributes. Then, the received image is compared with an image stored in association with a domain including the shooting position of the received image. On condition that the shooting condition lies within a predetermined range and the degree of similarity of images determined is off a predetermined range as the results of the comparison, the domain is divided, and the received image is stored in association with the divided domain, thereby creating an image database.

Abstract: An image processing apparatus for processing input image data selects, as a comparative cluster to be compared with a pixel of interest in the input image data, a cluster for which an absolute value of a difference between a component sum of the pixel of interest and a component sum of a representative value of the cluster is not more than a first threshold value. And the apparatus calculates a color distance between the pixel of interest and the representative value of each of the comparative clusters. The apparatus classifies the pixel of interest to a cluster corresponding to a minimum value of the color distances, if the minimum value of the color distances is not more than a second threshold value and generates a new cluster to which the pixel of interest is to be classified, if all the color distances are larger than the second threshold value.

Abstract: A method of recognizing an object in an image is provided, the method comprises the following steps. The image having the object is provided, and principal traits of the object are encoded in order to generate a first trait code. The first trait code is compared with data stored in a database so as to obtain a plurality of differences. A minimum of the plurality of differences is found. This method can be applied to synthesize human faces.

Abstract: A paper sheet identification apparatus capable of identifying an authenticity of a watermark area formed on a paper sheet is provided without increasing the cost. The paper sheet identification apparatus includes: a light receiving part receiving reflected light from a watermarked image formed on a paper sheet to be conveyed, a converter converting the reflected light from the watermarked image received by the light receiving part for each pixel as a unit of a predetermined size including color information having brightness; and an identification processing part identifying the authenticity of the watermarked image based on a correlation coefficient, which is calculated from a density value for each pixel converted by the converter and a density value for each pixel by the transmitted light from the watermarked image of the bill serving as a reference.

Abstract: An apparatus and method for encoding an image by skipping discrete cosine transform (DCT), and an apparatus and method for decoding an image by skipping the DCT. An image encoding apparatus may classify residual data into groups, and may encode the residual data using a representative value of each of the groups and group identification information that is used to identify the groups.

Abstract: Technologies are generally described for methods and systems for generating a representation of a skin condition. The method may include receiving first image data relating to a first image of a skin condition and second image data relating to a second image of the skin condition. The method may further include performing a component analysis of the first and second image data to generate a first and second weighted component analysis results for the first and second image data. The weighted component analysis results may represent the respective image data as a weighted sum of components. The method may further include performing a sampling analysis to the weighted component analysis results to generate a weighted image representation for the skin condition. The weighted image representation may represent the skin condition as a weighted sum of the components.

Abstract: Systems and methods are disclosed for identifying objects captured by a depth camera by condensing classified image data into centroids of probability that captured objects are correctly identified entities. Output exemplars are processed to detect spatially localized clusters of non-zero probability pixels. For each cluster, a centroid is generated, generally resulting in multiple centroids for each differentiated object. Each centroid may be assigned a confidence value, indicating the likelihood that it corresponds to a true object, based on the size and shape of the cluster, as well as the probabilities of its constituent pixels.

Abstract: The present invention relates to the field of management of image data in data storage. In particular, the present invention relates to a method and device for automatic detection of duplicate images in data storage and corresponding device while taking into account user perception of what he considers to be duplicate images, which method and device are particularly efficient with regard to the personalized, automatic management of large amounts of image data.

Abstract: The performance of image processing is assured by executing region segmentation of an image at high speed. Pixels are classified into clusters conforming to a feature such as color. In a case where a suitable cluster for classifying a pixel is undefined, a new cluster is defined and the pixel is classified into this cluster. An upper-limit value on number of clusters is set prior to classification. If the number of clusters exceeds the upper-limit value, clusters already defined are merged. This limits an increase in the number of clusters.

Abstract: System and method for determining a classifier to discriminate between two classes—object or non-object. The classifier may be used by an object detection program to detect presence of a 3D object in a 2D image. The overall classifier is constructed of a sequence of classifiers, where each such classifier is based on a ratio of two graphical probability models. A discreet-valued variable representation at each node in a Bayesian network by a two-stage process of tree-structured vector quantization is discussed. The overall classifier may be part of an object detector program that is trained to automatically detect different types of 3D objects. Computationally efficient statistical methods to evaluate overall classifiers are disclosed. The Bayesian network-based classifier may also be used to determine if two observations belong to the same category.

Abstract: A system and method for generating groups of cluster spines for display are provided. One or more concepts for each cluster in a set of clusters are generated. Spines are formed from at least a portion of the clusters based on the concepts. At least one spine unique from all other spines is placed. One or more spine groups are generated by positioning at least one unplaced spine in relation to one of the placed unique spines. The spine groups are displayed.

Abstract: A method, of selectively reading less than all information from an image sensor for which member-pixels of a subset of the entire set of pixels are individually addressable, may include: sampling information from a targeted member-pixel of the subset without having to read information from the entire set of pixels; and adaptively reading information from another one or more but fewer than all member pixels of the entire set based upon the sampling information without having to read all pixels on the image sensor. A related digital camera may include features similar to elements of the method.

Abstract: Multimodal data mining in a multimedia database is addressed as a structured prediction problem, wherein mapping from input to the structured and interdependent output variables is learned.

Abstract: A system and method for generating an image representation are provided. The image is modeled as a set of mixture weights, one for each of a set of reference image models, such as Gaussian mixture models (GMMs). The weights are derived by optimizing an objective function in which each reference image model is associated with its respective weight.

Abstract: Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

Abstract: A method and a system for searching for a global minimum are provided. First, a subclass of a plurality of space points in a multidimensional space is clustered into a plurality of clusters through a clustering algorithm, wherein each of the space points is corresponding to an error value in an evaluation function. Then, ellipsoids for enclosing the clusters in the multidimensional space are respectively calculated. Next, a designated space corresponding to each of the ellipsoids is respectively inputted into a recursive search algorithm to search for a local minimum among the error values corresponding to the space points within each designated space. Finally, the local minimums of all the clusters are compared to obtain the space point corresponding to the minimum local minimum.

Abstract: Techniques are disclosed for learning and modeling a background for a complex and/or dynamic scene over a period of observations without supervision. A background/foreground component of a computer vision engine may be configured to model a scene using an array of ART networks. The ART networks learn the regularity and periodicity of the scene by observing the scene over a period of time. Thus, the ART networks allow the computer vision engine to model complex and dynamic scene backgrounds in video.

Abstract: In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, for example, a same texture, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.

Abstract: An image processing device includes an information acquisition section that acquires a photographing position of a photographed image or a position of an imaging device as coordinate information, a distribution state acquisition section that acquires a distribution state of a plurality of pieces of coordinate information acquired as the coordinate information, and a keyword assignment section that assigns a keyword that corresponds to the acquired distribution state to the photographed image.

Abstract: A method, system and computer product for visualizing affinities between objects. The method includes the steps of: forming a representation of a minimum spanning tree where the minimum spanning tree connects the plurality of objects based on a pairwise distance between the plurality of objects; forming a hierarchical cluster of the plurality of objects where the hierarchical cluster includes a level; agglomerating the plurality of objects based on the pairwise distance; displaying a view of the representation of the minimum spanning tree in a graphical user interface; receiving a user selection of a parameter containing a hierarchical level; and identifying, in the view, a target cluster that corresponds to the hierarchical level; where at least one of the steps is carried out using a computer device so that affinities between the plurality of objects are visualized.

Abstract: A face cartooning system is described. In one implementation, the system generates an attractive cartoon face or graphic of a user's facial image. The system extracts facial features separately and applies pixel-based techniques customized to each facial feature. The style of cartoon face achieved resembles the likeness of the user more than cartoons generated by conventional vector-based cartooning techniques. The cartoon faces thus achieved provide an attractive facial appearance and thus have wide applicability in art, gaming, and messaging applications in which a pleasing degree of realism is desirable without exaggerated comedy or caricature.

Abstract: Described is a system for rapid directed area search utilizing particle swarm optimization. The system first extracts salient regions from an input image. The system then detects regions of interest from the salient regions utilizing particle swarm optimization, wherein a swarm of software agents, or particles, cooperate to locate an objective function optima, or region of interest, in an image. A set of local feature descriptors are then extracted from the image, wherein a local feature descriptor corresponds to a neighborhood surrounding a point of interest in a region of interest in the image. Additionally, the set of local feature descriptors are clustered hierarchically into a database so that a closest match between a new input image and a stored image can be determined. Finally, the matching regions of the two images are registered to align matching regions to allow detection of changes between the images.

Abstract: An information processing device includes: a clustering standard selection unit selecting a clustering standard, from clustering standards categorizing items into a plurality of clusters, including a first number or more known type clusters in which the probability that an item belonging to a cluster is known to a user is equal to or greater than a first threshold value and a second number or more unknown type clusters in which the probability is equal to or less than a second threshold value which is less than the first threshold value; and an exhibit control unit controlling the exhibit of a cluster or an item based on the selected clustering standard.

Abstract: A lamp clip (LC) includes a support pin (SP) making contact with and supporting an optical sheet such as a diffusion plate (46), and also includes ribs (RB) rising from the outer periphery of the support pin (SP) and reinforcing the support pin (SP).

Abstract: A system, method, and computer program product for detecting anomalies in an image. In an example embodiment the method includes partitioning each image of a set of images into a plurality of image local units. The method further includes clustering all local units in the image set into clusters, and consequently assigning a class label to each local unit based on the clustering results. The local units with identical class labels having at least one substantially related image feature. Further, the method includes assigning a weight to each of the local units based on a variation of the class labels across all images in a set of images. The method further includes performing a clustering over all images in the set by using a distance metric that takes the learned weight of each local unit into account, then determining the images that belong to minorities of the clusters as anomalies.

Abstract: A system for meta-classification having a training phase mechanism and an operational phase mechanism. The training phase mechanism may have a detection and tracking module, a classifier section connected to the detection and tracking module, a feature synthesis module connected to the classifier section, a labeling module connected to the feature synthesis module and a training data module connected to the labeling module. The operational phase mechanism may have a detection and tracking module, a classifier section connected to the detection and tracking module, a feature synthesis module connected to the classifier section and a meta-classification module connected to the feature synthesis module and the training module. The training phase mechanism may provide parameters and settings to the operational phase mechanism.

Abstract: A system, method, and computer program product for recognizing hand postures are described. According to one aspect, a set of training images is provided with labels identifying hand states captured in the training images. Inner Distance Shape Context (IDSC) descriptors are determined for the hand regions in the training images, and fed into a Support Vector Machine (SVM) classifier to train it to classify hand shapes into posture classes. An IDSC descriptor is determined for a hand region in a testing image, and classified by the SVM classifier into one of the posture classes the SVM classifier was trained for. The hand posture captured in the testing image is recognized based on the classification.

Abstract: An image processing apparatus includes: a sequence creating section configured to create a plurality of sequences in such a manner that one sequence includes consecutive face images of a same person in video image data; a similarity calculating section configured to calculate a first similarity of each pair in a plurality of face image dictionaries created for each sequence and a second similarity of each pair of each face image dictionary and a predetermined plurality of dictionaries; a similarity correcting section configured to correct the calculated and obtained plurality of first similarities by the second similarities; and a face clustering section configured to compare the plurality of first similarities corrected by the similarity correcting section with a predetermined threshold to cluster the plurality of face image dictionaries.

Abstract: A system and method for displaying an image are provided. In one embodiment, the method includes receiving a data stream representing a frame of an image. The data stream may indicate a first color pixel cluster corresponding to a first color and a second color pixel cluster corresponding to a second color. The first color pixel cluster and the second color pixel cluster may be displayed. The first color pixel cluster may be different from the second color pixel cluster.

Abstract: A method for calculating connectivity of a space having a number of objects using a computer system includes establishing a character matrix having a number of elements in accordance with the objects. A label matrix is established, and the character matrix is divided into blocks. Connectivity of the elements of each of the blocks is calculated, and then the elements are grouped into regions based on the values of the elements. Each of the regions is labeled and enhanced. Connectivity of the regions is calculated, and the regions are grouped into larger regions based on the values of the elements.

Abstract: An information management apparatus includes a characteristic-amount management unit managing a characteristic amount; a characteristic-amount association unit maintaining an association with a similar characteristic amount for each of the characteristic amounts; a space index management unit managing a space index for the characteristic amounts managed by the characteristic-amount management unit; a partial space determination unit determining a partial space in the space index to which a first characteristic amount belongs in accordance with a request for retrieving a characteristic amount similar to the first characteristic amount; and a similarity determination unit calculating similarity between the first characteristic amount and a second characteristic amount and between the first characteristic amount and a third characteristic amount associated with the second characteristic amount by the characteristic-amount association unit, and determines which characteristic amounts are similar to the first char

Abstract: A computer implemented method, apparatus, and computer program product for generating video based cohorts. Digital video data is processed to identify a set of size and shape based attributes associated with the set of objects. The digital video data comprises metadata describing the set of objects. A size and shape attribute comprises an attribute describing a shape associated with a portion of an object or a size measurement of the portion of the object. The set of size and shape based attributes are analyzed using cohort criteria to form a result. The cohort criteria specify attributes that are associated with members of a given cohort. A set of cohorts is generated based on the result. Each cohort in the set of cohorts comprises a subset of objects from the set of objects that share at least one size and shape based attribute in common.

Abstract: Various embodiments of a system and methods for decomposing an image into reflectance and shading components are described. An image decomposition module may represent an image as a product of the reflectance and shading components of the image. Data representing the image may be transformed into an image gradient containing both reflectance and shading component gradients. A normal vector perpendicular to the direction of the reflectance component gradient may be determined, based on a difference between the color channels of the image gradient. The image gradient may be modified according to the normal vector to eliminate the reflectance component gradient. The shading component gradient may be determined from the modified image gradient. The shading component gradient may be transformed into the shading component. The reflectance component may be determined by substituting the shading component into the representation of the image which is a product of the reflectance and shading components.

Abstract: A method and system of matching features in a pair of images using line signatures. The method includes determining a first similarity measure between a first line signature in a first image in the pair of images and a second line signature in a second image in the pair of images; determining a second similarity measure between the first line signature in the first image and a third line signature in the second image; comparing the first similarity measure with a first threshold value; comparing a difference between the first similarity and the second similarity with a second threshold value; and if the first similarity measure is greater than the first threshold value and the difference between the first similarity and the second similarity is greater than the second threshold value, the first line signature and the second line signature produce a match.

Abstract: A method for performing image recognition is disclosed. The method includes obtaining a collection of pixels and grouping at least some of the pixels into a set of cluster features based on gradient magnitude. For each cluster feature in the set, statistical variables are generated. The statistical variables represent a collective property of the pixels in the cluster feature. The statistical variables are utilized as a basis for comparing the collection of pixels to a different collection of pixels.

Abstract: The disclosure is related to a system and method for learning robust clothing clustering based on a cluster ensemble technique applied to the clothing features of images to improve clustering of images. Different types of clothing features that are complementary to each other are computed to provide extensive description of the clothing in the images. Multiple partitions are computed based on the clothing features to generate a cluster ensemble set. A consensus function is applied to the multiple partitions to generate a final clothing consensus clustering that encompasses the information contained in the multiple partitions. A system and method are disclosed for clustering images based on the clothing of one or more persons in the images.

Abstract: An apparatus for providing pattern detection may include a processor. The processor may be configured to iteratively test different models and corresponding scales for each of the models. The models may be employed for modeling parameters corresponding to a visually detected data. The processor may be further configured to evaluate each of the models over a plurality of iterations based on a function evaluation of each of the models, select one of the models based on the function evaluation of the selected one of the models, and utilize the selected one of the models for fitting the data.

Abstract: A method and system for performing gesture recognition of a vehicle occupant employing a time of flight (TOF) sensor and a computing system in a vehicle. An embodiment of the method of the invention includes the steps of receiving one or more raw frames from the TOF sensor, performing clustering to locate one or more body part clusters of the vehicle occupant, locating the palm cluster of the vehicle occupant, calculating the location of the tip of the hand of the vehicle occupant, determining whether the hand has performed a dynamic or a static gesture, retrieving a command corresponding to one of the determined static or dynamic gestures, and executing the command.

Abstract: According to one embodiment, a face image list display process module classifies face images, which are extracted from video content data, into a first face image group belonging to a predetermined attribute section and a second face image group belonging to sections other than the predetermined attribute section. The face image list display process module displays, on a face thumbnail display area, the face images belonging to one group of the first face image group and the second face image group in such a manner that the face images belonging to the one group are distinguished from the face images belonging to the other group of the first face image group and the second face image group.

Abstract: A digital filter bank having a number J?1 of stages is disclosed. For each integer j such that 1?j?J, the j-th stage includes a plurality of filtering units (20, 21) each receiving an input signal of the j-th stage. These filtering units include a low-pass filtering unit (20) using real filtering coefficients and at least one band-pass filtering unit (21) using complex filtering coefficients. Following each band-pass filtering unit of the j-th stage, a respective modulus processing unit (25) generates a processed real signal as a function of squared moduli of complex output values of the band-pass filtering unit. The input signal of the first stage is a digital signal supplied to the digital filter bank, while for 1<j?J, the input signal of the j-th stage includes the processed real signal generated by at least one modulus processing unit of the (j?1)-th stage.

Abstract: A method of segmenting a sequence of video images according to scene activity, the method comprising: defining a first series of nodes in a first multi-dimensional space, each node corresponding to an image of the sequence of video images; defining a transformation function that maps each of the first series of nodes to a corresponding node in a second multi-dimensional space having a lower dimensionality than the first multi-dimensional space; applying said transformation function to each of the first series of nodes to define a second series of respective nodes in the second multi-dimensional space; applying a data clustering algorithm to the second series of nodes to identify clusters of nodes within the second multi-dimensional space, the data clustering algorithm being constrained by a measure of feature distance between a pair of clusters of nodes and a measure of temporal distance between the pair of clusters of nodes; determining a representative image from each cluster of nodes and plotting each repr

Abstract: Clustering algorithms such as k-means clustering algorithm are used in applications that process entities with spatial and/or temporal characteristics, for example, media objects representing audio, video, or graphical data. Feature vectors representing characteristics of the entities are partitioned using clustering methods that produce results sensitive to an initial set of cluster seeds. The set of initial cluster seeds is generated using principal component analysis of either the complete feature vector set or a subset thereof. The feature vector set is divided into a desired number of initial clusters and a seed determined from each initial cluster.

Abstract: A method for predicting results for input data based on a model that is generated based on clusters of related characters, clusters of related segments, and training data. The method comprises receiving a data set that includes a plurality of words in a particular language. In the particular language, words are formed by characters. Clusters of related characters are formed from the data set. A model is generated based at least on the clusters of related characters and training data. The model may also be based on the clusters of related segments. The training data includes a plurality of entries, wherein each entry includes a character and a designated result for said character. A set of input data that includes characters that have not been associated with designated results is received. The model is applied to the input data to determine predicted results for characters within the input data.

Abstract: Embodiments described herein facilitate or enhance the implementation of image recognition processes which can perform recognition on images to identify objects and/or faces by class or by people.

Abstract: A dictionary creating apparatus registers probability distributions each including an average vector and a covariance matrix, in a dictionary. The dictionary creating apparatus organizes plural distribution profiles of character categories having similar feature vectors into one typical distribution profile, and registers the typical distribution profile and the character categories to be organized, associated with each other, in the dictionary, without registering eigenvalues and eigenvectors of all character categories, associated with each other, in the dictionary.

Abstract: The invention relates to a method for matching an object model to a three-dimensional point cloud, wherein the point cloud is generated from two images by means of a stereo method and a clustering method is applied to the point cloud in order to identify points belonging to respectively one cluster, wherein model matching is subsequently carried out, with at least one object model being superposed on at least one cluster and an optimum position of the object model with respect to the cluster being determined, and wherein a correction of false assignments of points is carried out by means of the matched object model. A classifier, trained by means of at least one exemplary object, is used to generate an attention map from at least one of the images. A number and/or a location probability of at least one object, which is similar to the exemplary object, is determined in the image using the attention map, and the attention map is taken into account in the clustering method and/or in the model matching.