Abstract: An object detection method includes an image acquisition step of acquiring an image including a target object, a layer image generation step of generating a plurality of layer images by one or both of enlarging and reducing the image at a plurality of different scales, a first detection step of detecting a region of at least a part of the target object as a first detected region from each of the layer images, a selection step of selecting at least one of the layer images based on the detected first detected region and learning data learned in advance, a second detection step of detecting a region of at least a part of the target object in the selected layer image as a second detected region, and an integration step of integrating a detection result detected in the first detection step and a detection result detected in the second detection step.

Abstract: The present invention aims to estimate a more consistent posture in regard to a multi-joint object. A target range image is first input, a human body region is extracted from the input range image, a target joint position candidate is calculated from the input range image, and a joint position is finally determined based on the calculated joint position candidate and a likelihood of each joint to estimate the posture. At this time, joint position permissible range information concerning inter-joint distance and angle of a human body model previously set by learning is obtained from a human body model storing unit, consistency is evaluated for a relation between the joint position candidates of a certain joint and other joint based on the obtained information, and thus the posture corresponding to the best combination of the joint positions is determined.

Abstract: In order to reduce limitations on an arrangement of a posture visualization system as a whole, a posture recognition unit recognizes, based on image data of a real space captured by an image capture unit from a predetermined viewpoint, a posture of an object in the image data of the real space. A posture visualization viewpoint setting unit sets a viewpoint different from the predetermined viewpoint. A posture visualization unit visualizes a mirror image of the posture of the object viewed from the viewpoint set by the posture visualization viewpoint setting unit based on the posture recognized by the posture recognition unit. A space display unit displays the mirror image of the posture of the object generated by the posture visualization unit and a mirror image of the real space at once.

Abstract: It is an object of the present invention to provide a user interface which facilitates confirmation of an area in an image and associated data corresponding to the area without requiring user's complicated operations. To achieve the object, an information processing apparatus comprises: an obtaining unit configured to obtain a first image including a plurality of objects, and information related to respective positions of the plurality of objects in the first image; and a determining unit configured to determine a position in case of shifting and displaying a second image indicating the plurality of objects with respect to the first image, based on the obtained positions of the plurality of objects.

Abstract: An information processing apparatus performs estimation processing on supervised data, and stores a relationship between teacher information and an estimation result. When unsupervised data is input, the information processing apparatus searches for supervised data high in degree of similarity in estimation result to unsupervised data, and generates teacher information from an estimation result of unsupervised data based on a relationship between teacher information and an estimation result about the detected supervised data.

Abstract: In order to reduce limitations on an arrangement of a posture visualization system as a whole, a posture recognition unit recognizes, based on image data of a real space captured by an image capture unit from a predetermined viewpoint, a posture of an object in the image data of the real space. A posture visualization viewpoint setting unit sets a viewpoint different from the predetermined viewpoint. A posture visualization unit visualizes a mirror image of the posture of the object viewed from the viewpoint set by the posture visualization viewpoint setting unit based on the posture recognized by the posture recognition unit. A space display unit displays the mirror image of the posture of the object generated by the posture visualization unit and a mirror image of the real space at once.

Abstract: An information processing apparatus comprises a storage unit configured to store correct answer data used to detect at least one portion of a detection object from an image and detection data detected as the at least one portion of the detection object from the image; a target determination unit configured to extract mismatching data between the correct answer data and the detection data, which exists within a predetermined range from a region in which the correct answer data and the detection data match, and determine the mismatching data as evaluation target data; an investigation unit configured to investigate property information of the evaluation target data; and an error determination unit configured to determine, based on the property information, whether the evaluation target data is error candidate data of the correct answer data.

Abstract: An image processing apparatus includes an acquisition unit, a first detection unit, a selection unit, and a correction unit. The acquisition unit acquires an image including a target object having a plurality of parts. The first detection unit detects a candidate region of each of the plurality of parts of the target object included in the acquired image using a previously learned model. The selection unit selects, based on the candidate region detected by the first detection unit, a first part having relatively high reliability and a second part having relatively low reliability from among the plurality of parts. The correction unit corrects the model by changing a position of the second part based on the first part selected by the selection unit.

Abstract: A recognition training apparatus according to the present invention includes a generation unit configured to generate relevance between a specific domain and a candidate of a recognition target based on ontology information expressing a concept structure of the specific domain, a selection unit configured to select the recognition target from the candidate of the recognition target based on the relevance generated by the generation unit, and a training unit configured to train a recognizer using training data regarding the recognition target selected by the selection unit.

Abstract: An apparatus includes an acquisition unit that acquires a first image based on a first parameter, and a second image based on a second parameter, a segmentation unit that segments each of the first and second images into a plurality of segments, an acquisition unit that acquires feature quantities from each of the plurality of segments formed by segmenting the first and second images, respectively, a calculation unit that calculates a reliability of each of the plurality of segments of the first image based on the feature quantities acquired from the first image, a classification unit that classifies the plurality of segments of the first image into a first field having a relatively high reliability and a second field having a relatively low reliability, and a determination unit that determines categories for the first and second fields based on the feature quantities acquired from the first and second images.

Abstract: An information presentation apparatus obtains monitoring information that indicates a state of a monitoring target, accumulates it in a storage unit, and detects, based on the monitoring information accumulated in the storage unit, the occurrence of an information presentation trigger event. The information presentation apparatus generates, based on the monitoring information accumulated in the storage unit, search data for searching for relevant information related to the detected event, uses the generated the search data to search for the relevant information, and generates presentation information for present the relevant information found by the searching.

Abstract: A Deep Neural Network (DNN) having a plurality of recognition tasks with different scales makes it possible to perform recognition processing in a network where identification layers are branched from one intermediate layer. An image recognition apparatus for recognizing a target includes a first acquisition unit configured to acquire from an input image a first intermediate feature amount for performing first identification, a first identification unit configured to perform the first identification based on the first intermediate feature amount, a second acquisition unit configured to acquire from the first intermediate feature amount a second intermediate feature amount for performing second identification having a larger scale than the first identification, and a second identification unit configured to perform the second identification based on the second intermediate feature amount.

Abstract: A system generates presentation information to a user based on a visual recognition target of the user, displays the generated presentation information on a first display unit of a first information processing device, determines detailed information related to the presentation information based on the presentation information displayed on the first display unit, and displays the determined detailed information on a second display unit of a second information processing device which is different from the first information processing device. Further, the system selects the presentation information in response to a designation from the user from among a plurality of the presentation information pieces displayed on the first display unit, and detects whether the selected presentation information is a selection mistake.

Abstract: When a human skin region is detected, misdetection or detection failure is prevented. An image processing apparatus of the present invention acquires an image from which a human body skin region is to be extracted, and estimates a human body part region in the image. The image processing apparatus then estimates a skin attribute of the estimated human body part region and corrects the human body part region based on the estimated skin attribute. The image processing apparatus extracts the skin region from the acquired image based on the skin attribute of the estimated human body part region and the corrected human body part region.

Abstract: To appropriately superimpose and display a virtual object on an image of a real space, an information processing apparatus according to exemplary embodiment of the present invention determines the display position of the virtual object based on information indicating an allowable degree of superimposition of a virtual object on each real object in the image of the real space, and a distance from a real object for which a virtual object is to be displayed in association with the real object.

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: In the present disclosure, an image parameter of an input image is changed, features is extracted from each of a plurality of generated images, a category of each region is determined based on the features in each image, and the results are integrated.

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: An image recognition apparatus is provided which comprises a first extracting means for extracting, from every registration image previously registered, a set of registration partial images of a predetermined size, and a second extracting means for extracting, from an input new image, a set of new partial images of a predetermined size. The apparatus further comprises a discriminating means for discriminating an attribute of the new partial image based on a rule formed by dividing the set of the registration partial images extracted by the first extracting means, and a collecting means for deriving a final recognition result of the new image by collecting discrimination results by the discriminating means at the time when the new partial images as elements of the set of the new partial images are input.

Abstract: A learning apparatus includes an acquisition unit, a creation unit, and a first learning unit. The acquisition unit acquires a plurality of leaning data sets including a plurality of images imaged by a plurality of imaging devices, and sensor information of the imaging devices when the plurality of respective images is imaged. The creation unit creates, from the plurality of the plurality of learning data sets, a plurality of subsets, wherein each of the plurality of subsets has a different combination of the plurality of images and the sensor information. The first learning unit learns a plurality of first classifiers respectively corresponding to the plurality of subsets based on the plurality of respective subsets.