Canon Kabushiki Kaisha

Masato Aoba

Takayuki Saruta

Masakazu Matsugu

Koichi Magai

Shunta Tate

Yasuhiro Komori

Yasuo Katano

Masafumi Takimoto

Koichi Takeuchi

There is provided with an image processing apparatus, for example, for image recognition. An extraction unit extracts a feature amount from a target image. An estimation unit estimates distribution of regions having attributes different from each other in the target image based on the feature amount.

Image processing apparatus, training apparatus, image processing method, training method, and storage medium

An image processing apparatus is operable to determine a focus target area of an image capturing apparatus. The apparatus comprises: an obtainment unit configured to obtain a first area to be a focus target in a first image captured by the image capturing apparatus at a first point in time, a detection unit configured to detect a second area to be a focus target candidate from a second image captured by the image capturing apparatus at a second point in time succeeding a first point in time; and a determination unit configured to determine, based on the first area and the second image, a focus target area in the second image from among one or more partial areas in the second area.

Image processing apparatus and method of controlling the same

An imaging apparatus configured to estimate information about an imaging setting of the imaging apparatus by using a trained model. The imaging apparatus includes a detection unit configured to detect a first operation for finalizing imaging and a second operation for interrupting imaging of the imaging apparatus in which the imaging setting has been made based on the information estimated by the trained model, and a training unit configured to update a connection weight of the trained model through reinforcement learning by determining, when the first operation is detected by the detection unit, a positive reward, and when the second operation is detected by the detection unit, a negative reward for the information having been estimated when the first or second operation is detected by the detection unit.

Imaging apparatus for controlling an imaging function that affects an imaging result

An image processing apparatus comprising: a learning unit configured to perform learning of a discriminator based on an image feature amount in a first image and a class of the first image defined by a first granularity; an evaluation unit configured to evaluate an image feature amount in a second image whose class is known by the discriminator after the learning; and a control unit configured to control the learning unit to, in a case in which the evaluation by the evaluation unit is that a predetermined criterion is not satisfied, perform the learning of the discriminator based on the image feature amount in the first image and a class of the first image defined by a second granularity coarser than the first granularity.

Image processing apparatus, image processing method, and non-transitory computer-readable storage medium that perform class identification of an input image using a discriminator that has undergone learning to perform class identification at different granularities

In a case where generating a training image of an object to be used to generate a dictionary to be referred to in image recognition processing of detecting the object from an input image, model information of an object to be detected is set, and a luminance image of the object and a range image are input. The luminance distribution of the surface of the object is estimated based on the luminance image and the range image, and the training image of the object is generated based on the model information and the luminance distribution.

Image processing apparatus and image processing method

An image processing apparatus includes a first learning unit configured to learn an identifier for identifying a class of a region formed by segmenting an image based on first training data, an evaluation unit configured to evaluate a result of identification of a class of the first training data by the identifier, a generation unit configured to generate second training data from the first training data based on an evaluation result by the evaluation unit, and a second learning unit configured to learn a plurality of identifiers different from the identifier learned by the first learning unit based on the second training data.

Image processing apparatus, image processing method, 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

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.

A work system includes an arm mechanism, a position and an orientation of which are configured to be changed so as to process an object placed on a work area, a member arranged at a distal end portion of the arm mechanism to receive a light projection pattern for calculating a position and an orientation of the distal end portion of the arm mechanism, and an irradiation unit configured to form the light projection pattern on the member and on the object by irradiating the member and the object with pattern light. In addition, an image sensing unit senses an image of the object and is fixed at a position independent of the arm mechanism, and a first calculation unit calculates a position and an orientation of the distal end portion of the arm mechanism and an irradiation plane of the pattern light based on the light projection pattern formed on the member. A second calculation unit calculates a position and an orientation of the object based on the calculated irradiation plane and the light projection pattern formed on the object.

Work system and information processing method

An information processing apparatus for performing recognition processing by a recognizer for a position and orientation of a work subject to undergo work by a working unit of a robot arm. The information processing apparatus including an obtaining unit adapted to obtain, for each of a plurality of positions and orientations of the work subject, a position and an orientation of the working unit in which the working unit can perform the work, and a restriction unit adapted to restrict a position and an orientation of the work subject used in the recognition processing by the recognizer to a position and an orientation of the work subject corresponding to the position and the orientation of the working unit that have been obtained by the obtaining unit.

Information processing apparatus, control method thereof and storage medium

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.

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Tokyo, Japan

Masato Aoba