Abstract: At least one model generation apparatus that generates a model representing a feature of specific data belonging to a specific category includes a selection unit configured to select, based on a degree of conformity between each of a plurality of pieces of specific data belonging to the specific category and each of a plurality of pieces of reference data included in a previously-set reference data group, at least one piece of reference data from the reference data group with respect to each piece of specific data, a parameter specifying unit configured to specify a parameter corresponding to the reference data selected by the selection unit with respect to each of the plurality of pieces of specific data, and a model generation unit configured to generate, as a model of the specific data, a parameter model indicating a distribution of at least one parameter specified by the parameter specifying unit.

Abstract: Embodiments of the present invention are directed to learning of an appropriate dictionary which has a high expression ability of minority data while preventing reduction of an expression ability of majority data. A dictionary generation apparatus which generates a dictionary used for discriminating whether data to be discriminated belongs to a specific category includes a generation unit configured to generate a first dictionary based on learning data belonging to the specific category and a selection unit configured to estimate a degree of matching of the learning data at each portion with the first dictionary using the generated first dictionary and select a portion of the learning data based on the estimated degree of matching, wherein the generation unit generates a second dictionary based on the selected portion of the learning data.

Abstract: An image processing apparatus includes: an acquisition unit configured to acquire a plurality of images each capturing an identical target and having a different attribute; a derivation unit configured to derive features from the plurality of images, using a first neural network; an integration unit configured to integrate the features derived from the plurality of images; and a generation unit configured to generate a higher quality image than the plurality of images from the feature integrated by the integration unit, using a second neural network.

Abstract: On the basis of a difference between first distribution regarding target task learning data as a plurality of learning data which belongs to a first category of a first task as a target task and second distribution regarding a plurality of learning data which belongs to the first category of source task learning data as learning data which belongs to the first category of a second task different from the first task, a transformation parameter for transforming the source task learning data is adjusted, the source task learning data is transformed based on the adjusted transformation parameter, and a classifier regarding the first task is generated based on the transformed source task learning data and the target task learning data.

Abstract: A data processing method includes setting a learning data set including a plurality of learning data having respective labels to an uppermost node of a decision tree, setting selection probabilities of the learning data, selecting a combination of the learning data from each of a plurality of sets of learning data assigned to nodes of the decision tree in accordance with the selection probabilities, determining a branch rule for dividing each of the sets of the learning data assigned to the nodes of the decision tree into at least two subsets using the selected combination of the learning data, and assigning the subsets divided in accordance with the branch rule to nodes in a lower level.

Abstract: To present a determination result with respect to input data and also a reason of the determination result to a user, an extraction unit configured to extract a plurality of feature amounts from an image including an inspection target object, a determination unit configured to determine an anomaly degree of the inspection target object on the basis of the extracted feature amounts, and an image generation unit configured to generate a defect display image representing a defect included in the inspection target object on the basis of contribution degrees of the respective feature amounts with respect to the determined anomaly degree are provided.

Abstract: An information processing apparatus comprises: a registration unit adapted to register information required to determine at least one specific pattern in an image; an input unit adapted to input image data; a first generation unit adapted to extract a predetermined feature distribution from the input image data, and to generate a first feature distribution map indicating the feature distribution; a second generation unit adapted to generate a second feature distribution map by applying a conversion required to relax localities of the first feature distribution map to the first feature distribution map; and a determination unit adapted to determine, using sampling data on the second feature distribution map and the registered information, which of the specific patterns the image data matches.

Abstract: A feature selection apparatus, which selects features to be used to discriminate an object by a discriminator using learning data including the object, extracts a plurality of partial data from the learning data, and obtains discrimination values obtained by controlling the discriminator to process the plurality of extracted partial data as features of the plurality of partial data. The feature selection apparatus evaluates the obtained features based on discrimination degrees on a discrimination space defined by the discriminator, and selects features to be used to discriminate the object from a plurality of features obtained in association with the plurality of partial data based on an evaluation result.

Abstract: An apparatus includes an extraction unit configured to extract a feature amount from each of a plurality of pieces of input data, a calculation unit configured to calculate, based on an identification model for identifying to which one of a plurality of labels each of the plurality of pieces of input data belongs, which is generated using the feature amount, a likelihood indicating how likely each of the plurality of pieces of input data belongs to the labels, and a presenting unit configured to present attribute information about the input data based on the feature amount and the likelihood.

Abstract: There is provided a classifier capable of classifying unknown abnormal data input to the classifier even if there is a small number of abnormal data used for the learning of the classifier. When learning parameters of the classifier, the specific category likelihood of normal patterns for learning relatively deviating from a group of normal patterns for learning is decreased relatively to the specific category likelihood of normal patterns for learning not relatively deviating from the group of normal patterns for learning, and the specific category likelihood of abnormal patterns for learning is decreased relatively to the specific category likelihood of the group of normal patterns for learning.

Abstract: An information processing apparatus, for training a classifier that classifies local regions of an object, includes a feature amount setting unit, a selection unit, and a training unit. The feature amount setting unit sets a feature amount to be used by the classifier. The selection unit selects a local region of the object based on a predetermined selection condition based on positions for obtaining the feature amount set by the feature amount setting unit. The training unit trains the classifier using the feature amount set by the feature amount setting unit and the local region selected by the selection unit.

Abstract: A multi view-point image composed of a great number of images according to a shape of an object is generated or an information processing method used for generating a three-dimensional model or performing image processing of arbitrary view-point object recognition is provided, and based on a plurality of captured images obtained by imaging of the object from a plurality of view points by an imaging means, a relative position and orientation with respect to the object relative to the imaging means for each of the plurality of view points is calculated, and based on the calculated plurality of relative positions and orientations, a missing position and orientation of the imaging means in a direction in which imaging by the imaging means is missing is calculated, and an image used for displaying the calculated missing position and orientation on a display means is generated.

Abstract: An object gripping apparatus includes an image capturing unit for capturing a region including a plurality of works, an obtaining unit for obtaining distance information of the region, a measurement unit for measuring three-dimensional positions/orientations of a plurality of gripping-candidate works out of the plurality of works based on the image and distance information, thereby generating three-dimensional position/orientation information, a selection unit for selecting a gripping-target work based on the three-dimensional position/orientation information, a gripping unit for gripping the gripping-target work, and an updating unit for updating the three-dimensional position/orientation information by measuring three-dimensional positions/orientations of the gripping-candidate works at a time interval during gripping of the gripping-target work.

Abstract: An information processing apparatus comprises: a registration unit adapted to register information required to determine at least one specific pattern in an image; an input unit adapted to input image data; a first generation unit adapted to extract a predetermined feature distribution from the input image data, and to generate a first feature distribution map indicating the feature distribution; a second generation unit adapted to generate a second feature distribution map by applying a conversion required to relax localities of the first feature distribution map to the first feature distribution map; and a determination unit adapted to determine, using sampling data on the second feature distribution map and the registered information, which of the specific patterns the image data matches.

Abstract: A plurality of images obtained by capturing a recognition target object from different viewpoint positions is acquired, and a portion set on the recognition target object in the image is received as a set portion for each of the images. A plurality of feature points is set in each of the images so as to set a larger number of feature points at the set portion than at an unset portion other than the set portion. The recognition target object is learned using image feature amounts at the feature points.

Abstract: An information processing apparatus comprises: a registration unit adapted to register information required to determine at least one specific pattern in an image; an input unit adapted to input image data; a first generation unit adapted to extract a predetermined feature distribution from the input image data, and to generate a first feature distribution map indicating the feature distribution; a second generation unit adapted to generate a second feature distribution map by applying a conversion required to relax localities of the first feature distribution map to the first feature distribution map; and a determination unit adapted to determine, using sampling data on the second feature distribution map and the registered information, which of the specific patterns the image data matches.

Abstract: In an image within which a face pattern is detected, when a ratio of a skin color pixel is equal to or smaller than a first threshold value in a first region and a ratio of a skin color pixel is equal to or greater than a second threshold value in a second r region, the vicinity of the first region is determined to be a face candidate position at which the face pattern can exist. Face detection is carried out on the face candidate position. The second region is arranged in a predetermined position relative to the first region.

Abstract: An information processing apparatus encodes an input pattern to a code including a plurality of bits, calculates reliabilities for respective bits of the code, generates a similar codes each similar to the code based on the reliabilities, and recognizes the input pattern based on the code and the similar codes.

Abstract: The robustness of discriminating results at each stage is improved in discrimination processing in which a plurality of stages of discriminators are used to identify an object. An information processing apparatus in which a plurality of stages of the discriminators are used to identify a class of an object, comprises a candidate class output unit that acquires as a candidate class a class discriminated at a first stage of the discriminators, and an extended class setting unit that sets in a second stage of the discriminators, a class of a second stage of the discriminators, which is defined as an extended partial space of a partial space defined by a candidate class in a discriminating space used in discriminating the candidate class by the first stage of the discriminators, as a class to be discriminated at this second stage of the discriminators.

Abstract: A method for information processing includes a learning process to generate a tree structured dictionary based on a plurality of patterns including a target object to be recognized. The method includes selecting a plurality of points from an input pattern based on a distribution of a probability that the target object to be recognized is present in the input pattern at each node of a tree structure generated in the learning process, and classifying the input pattern into a branch based on a value of a predetermined function that corresponds to values of the input pattern at selected plurality of points.