A method for learning representations from clouds of points data includes encoding clouds of points data into at least one representation by creating at least one tensor representation out of the clouds of points data. The method further includes using a loss function that utilizes a noisy reconstruction for reducing overfitting.

In one implementation, a computer-implemented system is provided for real- time video processing. The system includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to perform operations. The at least one processor is configured to receive real-time video generated by a medical image system, the real-time video including a plurality of image frames, and obtain context information indicating an interaction of a user with the medical image system. The at least processor is also configured to perform an object detection to detect at least one object in the plurality of image frames and perform a classification to generate classification information for at least one object in the plurality of image frames. Further, the at least one processor is configured to perform a video manipulation to modify the received real-time video based on at least one of the object detection and the classification. Moreover, the processor is configured to invoke at least one of the object detection, the classification, and the video manipulation based on the context information.

In one implementation, a computer-implemented system is provided for real- time video processing. The system includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to perform operations. The at least one processor is configured to receive real-time video generated by a medical image system, the real-time video including a plurality of image frames, and obtain context information indicating an interaction of a user with the medical image system. The at least processor is also configured to perform an object detection to detect at least one object in the plurality of image frames and perform a classification to generate classification information for at least one object in the plurality of image frames. Further, the at least one processor is configured to perform a video manipulation to modify the received real-time video based on at least one of the object detection and the classification. Moreover, the processor is configured to invoke at least one of the object detection, the classification, and the video manipulation based on the context information.

A method and apparatus for processing an image signal, an electronic device, and a computer-readable storage medium. The method includes: obtaining a digital image signal of a target image, the target image including object imaging corresponding to an object, identifying a first area of the object imaging in the target image from the digital image signal, removing the object imaging from the target image based on the first area, to obtain a background image corresponding to an original background, performing image inpainting processing on the first area of the background image to obtain a filled image, the filled image including the original background and a perspective background connected to the original background, identifying a second area in the object imaging, and removing an imaging portion corresponding to the second area from the object imaging, and superimposing the obtained adjusted object imaging on the first area.

A method and apparatus for processing an image signal, an electronic device, and a computer-readable storage medium. The method includes: obtaining a digital image signal of a target image, the target image including object imaging corresponding to an object, identifying a first area of the object imaging in the target image from the digital image signal, removing the object imaging from the target image based on the first area, to obtain a background image corresponding to an original background, performing image inpainting processing on the first area of the background image to obtain a filled image, the filled image including the original background and a perspective background connected to the original background, identifying a second area in the object imaging, and removing an imaging portion corresponding to the second area from the object imaging, and superimposing the obtained adjusted object imaging on the first area.

Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

Disclosed are a semantic image extrapolation method and a semantic image extrapolation apparatus. The present invention provides a technique for generating an empty region for image-extension in an image by using an extrapolated segmentation map and an inpainting technique. The present invention is to provide, considering that there is no information in an empty region for image-extension in an image, a semantic image extrapolation method, of first generating an extrapolated segmentation map on the basis of a segmentation map from an input image, and filling the empty region for image-extension in the image with information on the basis of the extrapolated segmentation map and the input image.

To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.

To improve the accuracy of the recognition processing used in an image sensor. A solid-state imaging device includes a pixel array, a converter, an image processing unit, a digital signal processing unit, and a control unit. The pixel array has a plurality of pixels that perform photoelectric conversion. The converter converts an analog pixel signal output from the pixel array into digital image data. The image processing unit performs image processing on the digital image data. The digital signal processing unit performs recognition processing on the digital image data output by the image processing unit. The control unit performs optimization regarding at least one acquisition processing operation among acquisition of the analog pixel signal, acquisition of the digital image data, and acquisition of a result of the recognition processing based on the result of the recognition processing.