Canon Kabushiki Kaisha

Takayuki Saruta

Masakazu Matsugu

Masato Aoba

Yusuke Mitarai

Katsuhiko Mori

Shunta Tate

Hiroto Yoshii

Yasuhiro Komori

Koichi Magai

Yasuo Katano

Koichi Takeuchi

According to the present disclosure, a weight parameter of a neural network is divided into a plurality of portions having a certain size and approximation is individually performed on the portions using a weighted sum of the codebook vectors.

Information processing apparatus, information processing method, and program

There is provided with an information processing apparatus. An acquisition unit acquires data. A detection unit detects a determination target from the data. A training unit performs training of a determiner for performing category determination concerning the determination target using information of the determination target detected by the detection unit and intermediate data being generated during detection processing by the detection unit and being different from the information of the determination target.

Information processing apparatus, information processing method, and medium

An apparatus includes a multitask setting unit configured to set a plurality of recognition tasks for which a multilayer neural network is trained, a neural network (NN) training unit configured to train the multilayer NN for the set plurality of recognition tasks, an NN evaluation unit configured to evaluate a training result of the multilayer NN, and a training parameter setting unit configured to set a training parameter in training the multilayer NN for the plurality of recognition tasks, based on a result of evaluation by the NN evaluation unit.

Information processing apparatus for training neural network for recognition task and method thereof

An image recognition method for an image recognition apparatus includes detecting, setting, acquiring, selecting, and specifying. At least one part of an identification target is selected from an identification target image. An inquiry region is set based on the detected part. A feature amount of the set inquiry region is acquired. At least one instance image corresponding to the identification target image is selected based on the acquired feature amount. A specific region of the identification target from the identification target image is specified based on the selected instance image.

Image recognition method, image recognition apparatus, and recording medium

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.

Image processing apparatus, image processing method, and storage medium to detect parts of an object

It aims to accurately identify a class concerning classification of an object for each area of an image. A plurality of subsets are created from a plurality of learning images, and an area discriminator for performing area discrimination of the learning images is learned for each subset created. Then, area discrimination of a plurality of learning evaluation images is performed by using the plurality of learned area discriminators. Further, an integrated identifier is learned based on an area discrimination result performed to the plurality of learning evaluation images and scene information previously associated with each of the plurality of learning evaluation images.

Image processing apparatus, image processing method and program

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.

Image processing apparatus and image processing method

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.

Information processing apparatus and training method

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.

Image processing apparatus, image processing method, and storage medium

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.

A probability at which a target object takes a target object state is acquired for each of target object states that the target object is allowed to take, and a distribution of the probabilities is acquired. A success rate is acquired, for each relative target object state being determined in advance for a position and orientation of an image capturing device, at which the target object is successfully identified from a captured image obtained by capturing the target object having the relative target object state, and a distribution of the success rates is acquired. A position and orientation that the device is to take is determined based on the distribution of the success rates acquired for each of a plurality of positions and orientations that the image capturing apparatus is allowed to take, and the distribution of the probabilities.

Information processing apparatus and information processing method

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.

Information processing method, information processing apparatus, and recording medium for identifying a class of an object by using a plurality of discriminators

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.

Recognition apparatus and method thereof, and computer program

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.

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.

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Tokyo, Japan

Takayuki Saruta