Abstract: An object recognition device includes: a model image processing unit having a feature point set decision unit setting a feature point set in a model image, and detecting the feature quantity of the feature point set, and a segmentation unit segmenting the model image; a processing-target image processing unit having a feature point setting unit setting a feature point in a processing-target image and detecting the feature quantity of the feature point; a matching unit comparing the feature quantities of the feature points set in the model image and in the processing-target image so as to detect the feature point corresponding to the feature point set, and executes a matching; and a determination unit determining the processing result in the matching unit so as to determine presence/absence of a model object in the processing-target image.

Abstract: An image processing apparatus includes: an image pyramid forming section configured to form an image pyramid by hierarchically creating layered image data including differently scaled images from inputted image data; a position calculating section configured to determine an in-image-pyramid position as a height position in the image pyramid to which template image data having an image portion of a target object at a fixed scale corresponds; an upper-layer-side likelihood calculating section configured to determine a likelihood for a target object by matching between upper-side layer image data directly above the in-image-pyramid position, and a state prediction; a lower-layer-side likelihood calculating section configured to determine a likelihood for a target object by matching between lower-side layer image data directly below the in-image-pyramid position, and a state prediction; and a true likelihood calculating section configured to determine a true likelihood from the likelihoods determined by the upper

Abstract: An information processing apparatus that executes processing for creating an environmental map includes a camera that photographs an image, a self-position detecting unit that detects a position and a posture of the camera on the basis of the image, an image-recognition processing unit that detects an object from the image, a data constructing unit that is inputted with information concerning the position and the posture of the camera and information concerning the object and executes processing for creating or updating the environmental map, and a dictionary-data storing unit having stored therein dictionary data in which object information is registered. The image-recognition processing unit executes processing for detecting an object from the image acquired by the camera with reference to the dictionary data. The data constructing unit applies the three-dimensional shape data registered in the dictionary data to the environmental map and executes object arrangement on the environmental map.

Abstract: An information processing apparatus that compares an input image with a model image to identify the subject of the input image with the subject of the model image. The apparatus includes feature value extraction means for setting feature points, each of which is on an edge of the model image and provided to extract a model image feature value, which is the feature value of the model image, and extracting the model image feature value from each of a plurality of feature value extraction areas in the neighborhood of each of the feature points, and matching means for checking if an input image feature value, which is the feature value of the input image, at the point that is on an edge of the input image and corresponds to the feature point matches any of the plurality of model image feature values at the feature point.

Abstract: An information processing apparatus that executes processing for creating an environmental map includes a camera that photographs an image, a self-position detecting unit that detects a position and a posture of the camera on the basis of the image, an image-recognition processing unit that detects an object from the image, a data constructing unit that is inputted with information concerning the position and the posture of the camera and information concerning the object and executes processing for creating or updating the environmental map, and a dictionary-data storing unit having stored therein dictionary data in which object information is registered. The image-recognition processing unit executes processing for detecting an object from the image acquired by the camera with reference to the dictionary data. The data constructing unit applies the three-dimensional shape data registered in the dictionary data to the environmental map and executes object arrangement on the environmental map.

Abstract: There is provided an information processing apparatus including a data pool generation section which generates an unknown data pool, a learning sample collection section which randomly collects a plurality of learning samples from the unknown data pool, a classifier generation section which generates a plurality of classifiers using the learning samples, an output feature quantity acquisition section which associates with the data, for each piece of the data, a plurality of output values, which are obtained by inputting the data into the plurality of classifiers to identify the data, as an output feature quantity represented in an output feature quantity space different from the feature quantity space, and a classification section which classifies each piece of the data into any one of a predetermined number of the classes based on the output feature quantity.

Abstract: There is provided an information processing device including: a global map acquiring unit that acquires at least a part of a global map representing positions of objects in a real space where a plurality of users are in activity; a local map generating unit that generates a local map representing positions of nearby objects detectable by a device of one user among the plurality of users; and an updating unit that updates the global map based on position data of objects included in the local map

Abstract: Disclosed herein is an object recognition device for recognition of an object that exists in an image, the device including: an edge image creator configured to create an edge image from a target image; a local feature extractor configured to extract a feature of each edge point on an edge image, and determine a base point and a support point from edge points in a local area defined for the edge image; a feature matching unit configured to implement, with reference to a memory, feature matching of a base point and a support point to thereby acquire a matching pair; and a matching pair determiner configured to determine final matching pairs by employing matching pairs having a highest matching cost among matching pairs obtained by the feature matching unit, wherein an object in a target image is identified based on matching pairs determined by the matching pair determiner.

Abstract: An image processing device that models, based on a plurality of frame images being results of time-sequential imaging of an object in motion, a motion of the object using a three-dimensional (3D) body configured by a plurality of parts is disclosed. The device includes: acquisition means for acquiring the frame images being the imaging results; estimation means for computing a first matrix of coordinates of a joint of the 3D body and a second matrix of coordinates of each of the parts of the 3D body, and generating a first motion vector; computing means for computing a second motion vector; and determination means for determining the 3D body.

Abstract: A learning device includes: a feature point extracting unit for extracting a feature point from each of multiple generated images; a feature point feature amount extracting unit for extracting feature point feature amount representing the feature of the feature point from the generated image; a whole feature amount calculating unit for calculating the whole feature amount representing the feature of the whole generated image from the feature point feature amount of the generated image based on a shared code book including generated feature amount to be commonly used for generation of an identifier for identifying each of different identified objects; and an identifier generating unit for generating the identifier based on the whole feature amount of the generated image, and a correct answer label representing whether the generated image is the positive image or the negative image.

Abstract: A learning device includes: a feature point extracting unit for extracting a feature point from each of multiple generated images made up of a positive image including an identified object, and a negative image excluding the identified object; a feature point feature amount extracting unit for extracting feature point feature amount representing the feature of the feature point from the generated image; a whole feature amount calculating unit for calculating the whole feature amount representing the feature of the whole generated image based on the feature point feature amount of a feature point existing on a feature point selection range determined based on the multiple generated images, of the generated image range; and an identifier generating unit for generating an identifier based on the whole feature amount of the generated image, and a correct answer label representing whether the generated image is the positive image or the negative image.

Abstract: A learning apparatus includes a learning section which learns, according as a learning image used for learning a discriminator for discriminating whether a predetermined discrimination target is present in an image is designated from a plurality of sample images by a user, the discriminator using a random feature amount including a dimension feature amount randomly selected from a plurality of dimension feature amounts included in an image feature amount indicating features of the learning image.

Abstract: An image processing apparatus is disclosed which processes moving images each divisible into a plurality of shots, the image processing apparatus including: a holding unit configured to hold discrimination models acquired by learning beforehand a first rule and a second rule from a moving image formed by a plurality of known shot groups which are made up of at least one shot each and from which a highlight is to be extracted in accordance with the discrimination models, the first rule governing relevance between the shots, the second rule governing relevance between frames within each of the shots; and an extraction unit configured to extract from a newly input moving image a shot group recognized as the highlight in accordance with the discrimination models held in the holding unit.

Abstract: The present invention is intended to provide a quick search of a plurality of sets. A common representative point setting block sets a common representative point common to sets on the basis of a feature vector stored in a vector information storage block and stores the set point into a common representative point information storage block. A neighborhood table group creation block creates a neighborhood table for each set on the basis of the vector information and common representative point information and stores the created table in a neighborhood table group storage block. A nearest neighborhood vector group search block, upon acquisition of an input vector, searches each set for a nearest neighborhood vector of the input vector on the basis of the vector information, the common representative point information, and the neighborhood table group and outputs the retrieved nearest neighborhood vector as a comparison result. The present invention is applicable to image processing apparatuses.

Abstract: The present invention provides a position estimation apparatus capable of determining whether an estimated value deviates from an actual value and trying a process to correct the estimated value if the estimated value is determined to be incorrect. The position estimation apparatus employs a position prediction unit configured to produce a prediction of the position of a mobile object having an odometry sensor mounted thereon, an environment observation unit configured to keep track of each feature point in the environment of the mobile object, a prediction-error check unit configured to determine whether the position prediction produced by the position prediction unit is correct or incorrect, a position-prediction correction unit configured to correct a wrong position prediction and a position/posture updating unit configured to update the position and/or posture of the mobile object on the basis of a correct position prediction.

Abstract: A learning device includes a feature-point extracting section extracting feature points from a generation image, a feature-point feature-quantity extracting section extracting feature-point feature-quantities representing features of the feature points, a total-feature-quantity generating section generating a total feature quantity represented by a multi-dimensional vector, and an identifier generating section generating an identifier using the total feature quantity and a true label indicating whether or not the generation image is a positive image or a negative image.

Abstract: An information processing apparatus includes a sight line detecting unit configured to detect a sight line of a viewer who is viewing content data and output sight line information which is a result of the detection, a viewing condition estimating unit configured to estimate a viewing condition of the viewer based on the output sight line information and visual attention information relating to the content data, and a content suggesting unit configured to make various suggestions with respect to the content data for the viewer based on the estimated viewing condition.

Abstract: An image processing apparatus includes: an image pyramid forming section configured to form an image pyramid by hierarchically creating layered image data including differently scaled images from inputted image data; a position calculating section configured to determine an in-image-pyramid position as a height position in the image pyramid to which template image data having an image portion of a target object at a fixed scale corresponds; an upper-layer-side likelihood calculating section configured to determine a likelihood for a target object by matching between upper-side layer image data directly above the in-image-pyramid position, and a state prediction; a lower-layer-side likelihood calculating section configured to determine a likelihood for a target object by matching between lower-side layer image data directly below the in-image-pyramid position, and a state prediction; and a true likelihood calculating section configured to determine a true likelihood from the likelihoods determined by the upper

Abstract: An object recognition device includes: a model image processing unit having a feature point set decision unit setting a feature point set in a model image, and detecting the feature quantity of the feature point set, and a segmentation unit segmenting the model image; a processing-target image processing unit having a feature point setting unit setting a feature point in a processing-target image and detecting the feature quantity of the feature point; a matching unit comparing the feature quantities of the feature points set in the model image and in the processing-target image so as to detect the feature point corresponding to the feature point set, and executes a matching; and a determination unit determining the processing result in the matching unit so as to determine presence/absence of a model object in the processing-target image.

Abstract: An information processing apparatus includes a model image obtaining unit configured to obtain a plurality of model images; a model image feature quantity extracting unit configured to extract feature quantities of the model images obtained by the model image obtaining unit; a matching unit configured to perform matching on the feature quantities of the model images extracted by the model image feature quantity extracting unit; and an identifying feature point extracting unit configured to extract, as a result of the matching performed by the matching unit, feature points having a low correlation with a predetermined model image in similar model images that are the model images having a predetermined number or more of the feature quantities that match feature quantities of the predetermined model image, the extracted feature points being regarded as identifying feature points used for identification of the respective similar model images.