Canon Kabushiki Kaisha

Fukashi Yamazaki

Daisuke Furukawa

Kiyohide Satoh

Daisuke Kotake

Keita Nakagomi

Yuta Narukiyo

An information processing apparatus includes an acquisition unit configured to acquire first information including information indicating a position and a size of a target in training image data and second information including information indicating a constraint regarding a region of the target, and a generation unit configured to generate a likelihood map indicating likeliness of being the target as ground-truth image data corresponding to the training image data based on the first information and the second information.

Information processing apparatus, method, and storage medium to generate a likelihood map

An information processing apparatus includes a first acquisition unit configured to acquire first region information about an object on first two-dimensional tomographic image data orthogonal to a first axis of three-dimensional tomographic image data, a second acquisition unit configured to acquire second region information about the object on second two-dimensional tomographic image data orthogonal to a second axis different from the first axis of the three-dimensional tomographic image data, and a generation unit configured to generate a three-dimensional likelihood map representing objectness of the object as three-dimensional ground truth image data corresponding to the three-dimensional tomographic image data based on the first region information and the second region information.

Information processing apparatus, information processing method, and non-transitory storage medium

An image processing apparatus includes an acquisition unit configured to acquire an image, and a classification unit configured to perform inference on the image by using a neural network and to output a classification result, wherein the neural network includes a common network that is common to a plurality of classes and is configured to extract a first feature from an input image, and a plurality of single-class networks each of which corresponds to a corresponding one of the plurality of classes and is configured to extract each of a plurality of second features based on the first feature. Each of the plurality of single-class networks has a single-class output layer that classifies the corresponding class.

Image processing apparatus, neural network training method, and image processing method

An image processing apparatus according to the present invention includes a first classification unit configured to classify a plurality of pixels in two-dimensional image data constituting first three-dimensional image data including an object into a first class group by using a trained classifier, and a second classification unit configured to classify a plurality of pixels in second three-dimensional image data including the object into a second class group based on a result of classification by the first classification unit, the second class group including at least one class of the first class group. According to the image processing apparatus according to the present invention, a user's burden of giving pixel information can be reduced and a region can be extracted with high accuracy.

Image processing apparatus, image processing system, image processing method, and storage medium for classifying a plurality of pixels in two-dimensional and three-dimensional image data

An information processing apparatus according includes an acquisition unit configured to acquire an image, an extraction unit configured to extract part of regions included in the image as a target region, a determination unit configured to determine a variation in density value of a pixel included in the target region such that an absolute value of the variation in density value of the pixel included in the target region is larger than an absolute value of a variation in density value of a pixel included in a region other than the target region, a generation unit configured to generate a training image in which a density value of the pixel included in the target region is changed based on the variation determined by the determination unit, and a training unit configured to train a identifier using the training image.

Information processing apparatus, information processing method, and storage medium

An image processing apparatus includes a first extraction unit configured to extract a first target region from an image using a trained classifier, a setting unit configured to set region information to be used in a graph cut segmentation method based on a first extraction result including the first target region, a second extraction unit configured to extract a second target region using the graph cut segmentation method based on the set region information, and a generation unit configured to generate a ground truth image corresponding to the image based on a second extraction result including the second target region.

Image processing apparatus, image processing method, and non-transitory computer-readable storage medium

An image processing apparatus includes a first classification unit configured to classify each of a plurality of pixels included in a three-dimensional medical image using a first classifier for classifying each pixel into a plurality of classes including a class representing a first target region, a determination unit configured to determine an image region including the first target region and a second target region from the three-dimensional medical image based on a first classification result, a second classification unit configured to classify each of a plurality of pixels included in the determined image region using a second classifier for classifying each pixel into a plurality of classes including at least either one of a class representing the first target region and a class representing the second target region, and an integration unit configured to integrate the first and the second classification results to acquire a third classification result.

Image processing system, image processing method, and non-transitory storage medium

A plurality of verification position/orientation candidates for a target object is set. A common structure model including a geometric feature of a part, among geometric features of a reference model representing a three-dimensional shape of the target object, that is common among the candidates is generated. An image including the target object is obtained. A position/orientation of the target object is estimated by verifying the common structure model and the reference model arranged at the plurality of verification position/orientation candidates, against the image.

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Kawasaki, Japan

Fukashi Yamazaki