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 recognition apparatus for recognizing a position and an orientation of a target object, inputs a captured image of the target object captured by an image capturing apparatus; detects a plurality of feature portions from the captured image, and to extract a plurality of feature amounts indicating image characteristics in each of the plurality of feature portions; inputs property information indicating respective physical properties in the plurality of feature portions on the target object; inputs illumination information indicating an illumination condition at the time of capturing the captured image; determines respective degrees of importance of the plurality of extracted feature amounts based on the respective physical properties indicated by the property information and the illumination condition indicated by the illumination information; and recognizes the position and the orientation of the target object based on the plurality of feature amounts and the respective degrees of importance thereof.

Abstract: In order to precisely identify a class relating to classification of an object at each of regions of an image, an image processing apparatus includes an acquisition unit configured to acquire image capturing information when the object has been captured, an estimation unit configured to estimate distribution relating to a degree of existence of each class which indicates classification of the object at each of predetermined regions of a captured image of the object based on the image capturing information acquired by the acquisition unit, and an identification unit configured to identify the class at each of the regions based on distribution information indicating the distribution estimated by the estimation unit and image information relating to the captured image.

Abstract: An information processing apparatus for matching a position and/or orientation of a measurement object with that of a model of the measurement object includes an acquisition unit configured to acquire a captured image of the measurement object, a calculation unit configured to calculate information indicating a surface shape of the measurement object based on the captured image, and a limitation unit configured to limit a position and/or orientation of the model based on the information indicating the surface shape.

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 dictionary for detection of an object is created from an image obtained by performing an image process, which depends on a first image process parameter, on the training image of the detection target object. The dictionary created based on the image process depending on the first image process parameter is determined, based on a result of detecting the object from an image obtained by performing an image process, which depends on the first image process parameter, on a photographed image based on the dictionary. A second image process parameter is determined, based on a result of detecting the object from an image obtained by performing an image process, which depends on the second image process parameter, on the photographed image using the determined dictionary.

Abstract: A feature amount cluster holding unit extracts respective feature amounts from N images sensed under N respective types of image sensing conditions, and manages the feature amounts extracted from each sensed image as a feature amount cluster in association with corresponding one of the image sensing conditions. A feature space distance calculation unit specifies the second feature amount cluster containing a feature amount similar to the feature amount of interest in the first feature amount cluster. An image sensing condition setting unit specifies sets of feature amounts associated with the N respective types of image sensing conditions from the first and second feature amount clusters. The image sensing condition setting unit specifies a set having a largest distance between feature amounts in the set among the specified sets. An image sensing condition associated with the specified set is output as an image sensing condition for discriminating the feature amount clusters.

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 for matching a position and/or orientation of a measurement object with that of a model of the measurement object includes an acquisition unit configured to acquire a captured image of the measurement object, a calculation unit configured to calculate information indicating a surface shape of the measurement object based on the captured image, and a limitation unit configured to limit a position and/or orientation of the model based on the information indicating the surface shape.

Abstract: A recognition apparatus for recognizing a position and an orientation of a target object, inputs a captured image of the target object captured by an image capturing apparatus; detects a plurality of feature portions from the captured image, and to extract a plurality of feature amounts indicating image characteristics in each of the plurality of feature portions; inputs property information indicating respective physical properties in the plurality of feature portions on the target object; inputs illumination information indicating an illumination condition at the time of capturing the captured image; determines respective degrees of importance of the plurality of extracted feature amounts based on the respective physical properties indicated by the property information and the illumination condition indicated by the illumination information; and recognizes the position and the orientation of the target object based on the plurality of feature amounts and the respective degrees of importance thereof.

Abstract: A feature amount cluster holding unit extracts respective feature amounts from N images sensed under N respective types of image sensing conditions, and manages the feature amounts extracted from each sensed image as a feature amount cluster in association with corresponding one of the image sensing conditions. A feature space distance calculation unit specifies the second feature amount cluster containing a feature amount similar to the feature amount of interest in the first feature amount cluster. An image sensing condition setting unit specifies sets of feature amounts associated with the N respective types of image sensing conditions from the first and second feature amount clusters. The image sensing condition setting unit specifies a set having a largest distance between feature amounts in the set among the specified sets. An image sensing condition associated with the specified set is output as an image sensing condition for discriminating the feature amount clusters.