Abstract: A method and system for generating a detector to detect a dominant color of an image is provided. A dominant color system trains a detector to classify colors as being dominant colors of images. The dominant color system trains the detector using a collection of training images. To train the detector, the dominant color system first identifies candidate dominant colors of the training images. The dominant color system then extracts features of the candidate dominant colors. The dominant color system also inputs an indication of dominance of each of the candidate dominant colors. The dominant color system then trains a detector to detect the dominant color of images using the extracted features and indications of dominance of the candidate dominant colors as training data.

Abstract: An approach to clustering a set of images based on similarity measures employs a fuzzy clustering paradigm in which each image is represented by a node in a graph. The graph is ultimately partitioned into subgraphs, each of which represent true clusters among which the various images are distributed. The partitioning is performed in a series of stages by identifying one true cluster at each stage, and removing the nodes belonging to each identified true cluster from further consideration so that the remaining, unclustered nodes may then be grouped. At the beginning of each such stage, the nodes that remain to be clustered are treated as all belonging to a single candidate cluster. Nodes are removed from this single candidate cluster in accordance with similarity and connectivity criteria, to arrive at a true cluster. The member nodes of this true cluster are then removed from further consideration, prior to the next stage in the process.

Abstract: A biometric apparatus for biometrically securing access to an electronic system is disclosed. Such an apparatus can be configured to include a hardware unit having an input area integrated therein for inputting at least one biometric attribute. The hardware unit can communicate with an electronic system, such as, for example, a building, a computer, a computer network, wireless network, ATM machine, electronic database, etc. A display area can be integrated with the hardware unit, wherein the display area displays data which prompts a user to input to the input area one or more biometric attributes randomly selected from a user profile containing biometric attributes of the user. A biometric module can also be associated with the hardware unit, wherein the biometric module permits the user to perform a user-desired activity, if one or more biometric attributes input by the user via the input area matches at least one biometric attribute randomly selected from the user profile.

Abstract: A face recognition system includes a component learning/extraction module, component classifier training module, knowledge base for component classification (KBCC), component extraction module (CEM), object identification training module (OITM), knowledge base for face identification (KBFI), and object identification module (OIM). The CEM receives image data of faces at various viewpoints and extracts outputs of classification of the component data, using the results of classifier training of the component data, stored in the KBCC. The OITM receives the outputs of classification of the component data and determines indicator component for each person by Bayesian estimation so that posterior probability of a predetermined attention class is maximized under the outputs of classification of the component data at various viewpoints. The KBFI stores indicator components for the individuals.

Abstract: A partition pattern template generation method for alignment receives a learning image and performs partition template generation using the learning image to generate a plurality of partition template result output. A partition template acceptance test is performed using the plurality of partition template results to generate partition templates or failure result. A partition template search method for alignment receives an alignment image and partition templates and performs a plurality of template search steps to generate a plurality of matching scores output. A partition integration method is performed using the plurality of matching scores to generate a partition template search result. A partition integration error self checking method receives a preliminary template search result position and a plurality of the matching scores. A matching score profile comparison is performed using the plurality of the matching scores and the expected matching score profile to generate the template search result.

Abstract: A face detection device comprises: a judgment area determining operable to judgment areas in an inputted image; and a face judgment unit operable to judge existence of a face image greater than a first predetermined size in the judgment areas. According to face directions, the judgment area determining unit determines a ratio of the inputted image to the judgment areas. The characteristics of face images can be effectively utilized.

Abstract: Systems and methods are described for detecting capture-intention in order to analyze video content. In one implementation, a system decomposes video structure into sub-shots, extracts intention-oriented features from the sub-shots, delineates intention units via the extracted features, and classifies the intention units into intention categories via the extracted features. A video library can be organized via the categorized intention units.

Abstract: A method for training a system for detecting multi-class objects in an image or a video sequence is described. A common ensemble of weak classifiers for a set of object classes is identified. For each object class, a separate weighting scheme is adapted for the ensemble of weak classifiers. A method for detecting objects of multiple classes in an image or a video sequence is also disclosed. Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.

Abstract: The present invention is directed to a method for automatic detection and segmentation of a target anatomical structure in received three dimensional (3D) volumetric medical images using a database of a set of volumetric images with expertly delineated anatomical structures. A 3D anatomical structure detection and segmentation module is trained offline by learning anatomical structure appearance using the set of expertly delineated anatomical structures. A received volumetric image for the anatomical structure of interest is searched online using the offline learned 3D anatomical structure detection and segmentation module.

Abstract: In a method of detection of different objects in an input image by application, to partial images cut at different positions in the input image, of a plurality of weak classifiers that evaluate whether a detection target image is an image of a predetermined object based on a histogram of values of characteristic quantities calculated from a plurality of sample images representing the predetermined object, the histogram is extended to multi-dimensions and a criterion for the evaluation by the weak classifiers is a multi-dimensional histogram representing histograms for the different objects in the form of vectors.

Abstract: Various technologies and techniques are disclosed that generate a teaching data set for use by a handwriting recognizer. Ink input is received from various ink sources, such as implicit field data, scripted untruthed ink, scripted truth ink, and/or ink from at least one other language in the same script as the target language for the recognizer. The ink input is used with various machine learning methods and/or other algorithmic methods to generate a teaching ink data set. Examples of the various machine learning methods include a character and/or word n-gram distribution leveling method, an allograph method, a subject diversity method, and a print and cursive data selection method. The teaching ink data is used by a handwriting trainer to produce the handwriting recognizer for the target language.

Abstract: A data processing apparatus processes input data and outputs processed data. The data processing apparatus includes a data processing section and a real-time learning section. The data processing section processes the input data by a predetermined processing method and outputs the processed data. The real-time learning section controls the data processing section such that the processing method is learned in real time. The data processing section processes the input data by the learned processing method so that the output data is improved as time elapses.

Abstract: In a first exemplary embodiment of the present invention, an automated, computerized method is provided for determining illumination flux in an image. According to a feature of the present invention, the method comprises the steps of performing a computer learning technique to determine spatio-spectral information for the images, and utilizing the spatio-spectral information to identify illumination flux.

Abstract: A camera is used to sense cargo in a cargo space. An image of at least a portion of a cargo space is captured using the camera, and a digital signal processor classifies the image as representing an empty cargo space or a non-empty cargo space. Additionally, a type of cargo represented by the captured image may also be classified by the digital signal processor and an indication of the type of cargo output.

Abstract: The invention concerns image data processing, through noise reduction comprising the following steps: associating a learning zone (ZA) with a reference point (Pref) of the image (IM); for each variable point (PC, PC?,) of the learning zone, evaluating a distance (d, d?,) between: values of points in a first window (f1) of the image, centered on the reference point, and values of points in a second window (f7, f?2,), of similar format as the format of the first window and centered on the variable point; repeating said distance calculation for all the points of the learning zone as successive variable points and estimating an average value to assign to the reference point, said average being weighted on the basis of the distances evaluated for each variable point.

Abstract: A face recognition apparatus and method using Principal Component Analysis (PCA) learning per subgroup, the face recognition apparatus includes: a learning unit which performs Principal Component Analysis (PCA) learning on each of a plurality of subgroups constituting a training data set, and then performs Linear Discriminant Analysis (LDA) learning on the training data set, thereby generating a PCA-based LDA (PCLDA) basis vector set of each subgroup; a feature vector extraction unit which projects a PCLDA basis vector set of each subgroup to an input image and extracts a feature vector set of the input image with respect to each subgroup; a feature vector storing unit which projects a PCLDA basis vector set of each subgroup to each of a plurality of face images to be registered, thereby generating a feature vector set of each registered image with respect to each subgroup, and storing the feature vector set in a database; and a similarity calculation unit which calculates a similarity between the input image

Abstract: Systems and methods are presented that determine components to use as examples to train a component-based face recognition system. In one embodiment, an initial component shape and size is determined, a training set is built, a component recognition classifier is trained, and the accuracy of the classifier is estimated. The component is then temporarily grown in each of four directions (up, down, left, and right) and the effect on the classifier's accuracy is determined. The component is then grown in the direction that maximizes the classifier's accuracy. The process can be performed multiple times in order to maximize the classifier's accuracy.

Abstract: Many problems in the fields of image processing and computer vision relate to creating good representations of information in images of objects in scenes. We provide a system for learning repeated-structure elements from one or more input images. The repeated-structure elements are patches that may be single pixels or coherent groups of pixels of varying shape, size and appearance (where those shapes and sizes are not pre-specified). Input images are mapped to a single output image using offset maps to specify the mapping. A joint probability distribution on the offset maps, output image and input images is specified and an unsupervised learning process is used to learn the offset maps and output image. The learnt output image comprises repeated-structure elements. This shape and appearance information captured in the learnt repeated-structure elements may be used for object recognition and many other tasks.

Abstract: Embodiments provide for an image capture device having a learning function. Briefly described, one embodiment comprises at least one manual control adapted to generate a control signal that controls image capture operation, the control operable by a user of the image capture device; at least one sensor adapted to sense an operating condition during image capture; a first element adapted to analyse inputs from the sensor to learn at least one associated operating condition, and adapted to analyse the control signal to learn a corresponding preference of the user; a second element adapted to predict a value corresponding to the control signal and the associated operating condition; and a third element adapted to compare the predicted value and the control signal, and adapted to determine a confidence level from the compared predicted value and control signal, wherein the confidence level corresponds to a degree of confidence in the predicted value.

Abstract: This invention relates to a novel technology of fingerprint verifications and identifications based on the accumulated knowledge base.

Abstract: In general, the present invention is directed to systems and methods for identifying behaviors of two or more animal using video. The invention includes a system with at least two video cameras coupled to a computer where the computer is configured to automatically perform animal segmentation and identification, animal body parts identification, and behavior identification.

Abstract: A method for classifying two-dimensional shapes comprising: creating a training dataset for the shape under study with a similar shape group and a dissimilar shape group; computing an average shape from the similar shape group; aligning all shapes in the database to the average shape; and classifying shapes into two clusters based on their similarity distances.

Abstract: Designs for cognitive memory systems storing input data, images, or patterns, and retrieving it without knowledge of where stored when cognitive memory is prompted by query pattern that is related to sought stored pattern. Retrieval system of cognitive memory uses autoassociative neural networks and techniques for pre-processing query pattern to establish relationship between query pattern and sought stored pattern, to locate sought pattern, and to retrieve it and ancillary data.

Abstract: A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier.

Abstract: A learning apparatus includes an image generator, a feature point extractor, a feature value calculator, and a classifier generator. The image generator generates, from an input image, images having differing scale coefficients. The feature point extractor extracts feature points from each image generated by the image generator. The feature value calculator calculates feature values for the feature points by filtering the feature points using a predetermined filter. The classifier generator generates one or more classifiers for detecting a predetermined target object from an image by means of statistical learning using the feature values.

Abstract: A learning method is disclosed for an article storage facility having an article storage rack including article storage units arranged in a rack lateral width direction and a vertical direction, a vertically movable lift, and a horizontal travel carriage associated with the vertically movable lift. A frontal view camera is positioned with respect to the article transfer device such as to capture an image of a detected member provided for each of the storage units from a rack fore-and-aft direction. An angular view camera is positioned with respect to the article transfer device such as to be displaced relative to the frontal view camera in the rack lateral width direction or the vertical direction and such as to capture an image of a detected member from a direction at an angle relative to the rack fore-and-aft direction.

Abstract: A method for predicting a processing result includes a process of performing a principal component analysis on a plurality of detected data obtained during a first standard processing, to construct a principal component analysis model; a process of obtaining residuals of the principal component analysis model as first residuals; a process of performing a second standard processing; and a process of obtaining a plurality of detected data from a plurality of detectors during the second standard processing. And, the detected data obtained during the second standard processing are applied to the principal component analysis model, to obtain second residuals. The method further includes a process of weighting the second residuals based on weighting references, and constructing a new multivariate analysis model with use of the weighted second residuals; and a process of predicting a processing result of the second standard processing with use of the multivariate analysis model.

Abstract: The present invention relates to a method of designating an object in an image. The method includes: designating a point inside the object in the image; segmenting the image into elementary regions; identifying an origin region to which the point belongs; constructing a graph of connectedness between the regions; calculating a function of membership in the object for the regions connected to the origin region, by combining various membership criteria; merging the origin region with its connected regions, a connected region being merged if the value of its membership function is greater than a predetermined threshold; wherein the steps of calculating membership functions of the connected regions and of merging is repeated for each new merged region until no merging is performed. One or more embodiments of the invention applies to image processing in order to perform the graphical designation of an object by an operation that is simple for a user.

Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.

Abstract: In a hierarchical neural network having a module structure, learning necessary for detection of a new feature class is executed by a processing module which has not finished learning yet and includes a plurality of neurons which should learn an unlearned feature class and have an undetermined receptor field structure by presenting a predetermined pattern to a data input layer. Thus, a feature class necessary for subject recognition can be learned automatically and efficiently.

Abstract: Methods and systems are provided for selecting features that will be used to recognize faces. Three-dimensional models are used to synthesize a database of virtual face images. The virtual face images cover wide appearance variations, different poses, different lighting conditions and expression changes. A joint boosting algorithm is used to identify discriminative features by selecting features from the plurality of virtual images such that the identified discriminative features are independent of the other images included in the database.

Abstract: A method and system for predicting customer behavior based on the geography of a data network are provided. Furthermore, a method and system for evaluating the training of a predictive algorithm to determine if the algorithm does not adequately take into consideration the influences of data network geography are also provided. The method and system generate frequency distributions of a customer database data set, training data set and testing data set and compare the frequency distributions of data network geographical characteristics to determine if there are discrepancies. If the discrepancies are above a predetermined tolerance, one or more of the data sets may not be representative of the customer database taking into account data network geographical influences on customer behavior. Thus, recommendations for improving the training data set and/or testing data set are then provided such that the data set is more representative of the data network geographical influences.

Abstract: In a system and method for generating a 3-dimensional representation of a portion of an organism, collecting training data, wherein the training data includes a first set of training data and a second set of training data. At least one statistical model having a set of parameters is built using the training data. The at least one statistical model is compared to a 2-dimensional image of the portion of the organism. At least one parameter of the set of parameters of the statistical model is modified based on the comparison of the at least one statistical model to the 2-dimensional image of the portion of the organism. The modified set of parameters representing the portion of the organism is passed through the statistical model.

Abstract: In an apparatus and method for generating a three-dimensional representation from a two-dimensional image, a memory device stores information for processing a two-dimensional image and for generating a three-dimensional image from the two-dimensional image, a processing device processes a digital representation of an image by generating a two-dimensional image from the digital representation and by generating a three-dimensional image corresponding to the two-dimensional image, and an output device outputs a three-dimensional image and a digital signal representation of the three-dimensional image.

Abstract: A method and apparatus for unsupervised learning of measures for matching objects between images from at least two non-overlapping cameras is disclosed The method includes collecting at least two pairs of feature maps, where the at least two pairs of feature maps are derived from features of objects captured in the images. The method further includes computing, as a function of at least two pairs of feature maps, at least one first and second match measures, wherein the first match measure is of a same class and the second match measure is of a different class.

Abstract: To provide an art capable of easily generating accurate teaching data from a small number of teacher images. The image of a standard object is captured to extract a characteristic value from the teacher image of the standard object through image processing while making a user input the already-known knowledge for a configuration of an object and an object such as a tolerance and entering the knowledge in a knowledge base. Then, context data in which various attributes which can be objects are described together with their semantics is generated in accordance with the characteristic value extracted from the teacher image of the standard object and the knowledge entered in the knowledge base. Moreover, attributes corresponding to types of characteristic values used for recognition processing are extracted from the context data entered in the knowledge base to generate the teaching data for the recognition processing.

Abstract: A data processing apparatus processes input data and outputs the processed data. The data processing apparatus includes a data processing section and a real-time learning section. The data processing section processes the input data by a predetermined processing method and outputs the processed data. The real-time learning section controls such that the processing method is learned in real time and the data processing section processes the input data by the learned processing method, so that the output data is improved as time elapses.

Abstract: One embodiment of the present invention provides a system that efficiently performs a belief-propagation (BP) operation. During this process, for each node i in a BP graph, the system iteratively performs the following operations. First, the system receives incoming messages mji at node i for all adjacent nodes j. Next, the system calculates the full product Pi of all incoming messages mji. The system then produces an outgoing message mij from node i to node j by, computing a partial product Pij of all incoming messages to node i except for the message from node j by dividing the Pi by the incoming message from node j. The system then combines Pij with a data function for node i and a smoothness function between node i and node j to produce outgoing message mij. Finally the system communicates outgoing message mij to node j.

Abstract: A plurality of landmarks, which are employed in statistical models, such as an ASM, that is, a plurality of landmarks that represent the shape and the like of a predetermined subject, such as a face, within an image, are detected. Discrimination regarding whether each pixel within the image is a point that represents a landmark is performed based on discrimination conditions. The discrimination conditions correspond to characteristic amounts, which are obtained by learning characteristic amounts of positions, which are known to be landmarks, and characteristic amounts of positions, which are known to not be landmarks, within a plurality of sample images of the subject, by a machine learning technique.

Abstract: An object tracking apparatus consisting of an imaging device, a signal processor for processing a video signal generated by the imaging device, and a display for displaying the result of processing performed by the signal processor. An intruding object is detected within a field under monitoring based on a video signal captured by the imaging device, and the imaging device is controlled in response to motions of the intruding object to track the object. The position of a visual field range of the imaging device is calculated, and panning and tilting of the imaging device and an imaging lens are controlled such that the calculated position of the visual field range of the imaging device does not include an imaging prohibited region positioned within the field under monitoring.

Abstract: A facial expression recognition system that uses a face detection apparatus realizing efficient learning and high-speed detection processing based on ensemble learning when detecting an area representing a detection target and that is robust against shifts of face position included in images and capable of highly accurate expression recognition, and a learning method for the system, are provided. When learning data to be used by the face detection apparatus by Adaboost, processing to select high-performance weak hypotheses from all weak hypotheses, then generate new weak hypotheses from these high-performance weak hypotheses on the basis of statistical characteristics, and select one weak hypothesis having the highest discrimination performance from these weak hypotheses, is repeated to sequentially generate a weak hypothesis, and a final hypothesis is thus acquired.

Abstract: A highly reliable defect automatic classifying method and apparatus capable of flexibly coping with a request for classifying a defect given by each user without having to collect lots of teach data items. A classifying class arrangement is defined by a user by combining classes supplied by the system itself or classes defined by the user and, further, a priori knowledge on the defect class is given by the user as a restriction so as to carry out restricted learning.

Abstract: A temporary self-organizing map in which classes are associated with respective vector points is first derived by a first learning section of a data learning apparatus. Thereafter, by use of, for example, an improved method incorporating concept of vicinity learning intp learning vector quantization, a second learning section modifies the temporary self-organizing map and obtains a final self-organizing map. By use of the final self-organizing map thus derived, image meaning determining processing is performed.

Abstract: A method of identifying at least one particular person of interest in a collection of digital images taken over time, includes providing the collection of digital images with each digital image containing one or more persons; storing in a digital database an appearance model having a set of features associated with the particular person of interest and a prior probability of the person of interest appearing in images by a particular photographer; and comparing the appearance model with features extracted from the digital images in the collection of digital images and using the prior probability and the appearance model to determine if the particular person of interest is in one or more digital images in the collection of digital images.

Abstract: The present invention relates to various apparatus, algorithms and methods for acquiring and processing images of a scene. Details of various aspects of the associated images are identified and may be utilized to generate various vehicular equipment control signals.

Abstract: A method and system for container identification are disclosed. The method comprises obtaining a plurality of digital images of a character sequence on the container, extracting the character sequences from the images, combining the character sequences into at least one identification code candidate, and selecting one of the candidates as the identification code. The system comprises at least one camera and a computer system that is electrically coupled to the camera, whereby when the computer system receives a trigger signal, said computer system takes a plurality of digital images from the camera, produces character sequences as partial recognition results for the plurality of digital images, and combines the partial recognition results together to produce the identification code.

Abstract: Methods and apparatus are provided for evoking perceptions of affordances in a user/virtual environment interface. The method involves recognizing the absence or inadequacy of certain sensory stimuli in the user/virtual environment interface, and then creating sensory stimuli in the virtual environment to substitute for the recognized absent or inadequate sensory stimuli. The substitute sensory stimuli are typically communicated to the user (e.g., visually and/or audibly) as properties and behavior of objects in the virtual environment. Appropriately designed substitute sensory stimuli can evoke perceptions of affordances for the recognized absent or inadequate sensory stimuli in the user/virtual environment interface.

Abstract: An image processing device includes: a smoothing section configured to extract a smoothing tap and smooth a target image on the basis of pixel values within the tap, the smoothing tap being of variable size and including plural pixels centered on each target pixel of the image; a class tap extracting section configured to extract a class tap including plural pixels centered on each target pixel in the smoothed image; a class code determining section configured to generate a code corresponding to a characteristic of variation of pixel values within the class tap, and determine a class code including a size of the smoothing tap and the code; and a pixel value computing section configured to read tap coefficients corresponding to the determined class code, and multiply pixel values forming a prediction tap extracted from the smoothed image, by the tap coefficients to calculate pixel values of a processed image.

Abstract: The present invention discloses methods and systems for discovering printers and shares on a computer network. Each domain on the network is identified, and each computer in the domain is identified. In addition, each printer connected to the computer and each share on the computer is identified. Shortcuts to the identified printers and shares are created on at least one computer on the network. Moreover, drivers are preferably installed on the computer for each printer for which a shortcut was created. In the event that the total number of resources (i.e., shares and/or printers) exceeds a threshold, then the process terminates. Otherwise, the present invention continues until all printers and shares on the network are identified, and the appropriate shortcuts are created. Thus, the present invention provides methods and systems for discovering resources on a network.

Abstract: An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules.