Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: 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.

Abstract: 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.

Abstract: 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.

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: 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: 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.

Abstract: 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.

Abstract: 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.