A neural network, a system using this neural network and a method for training a neural network to output a description of the environment in the vicinity of at least one sound acquisition device on the basis of an audio signal acquired by the sound acquisition device, the method including: obtaining audio and image training signals of a scene showing an environment with objects generating sounds, obtaining a target description of the environment seen on the image training signal, inputting the audio training signal to the neural network so that the neural network outputs a training description of the environment, and comparing the target description of the environment with the training description of the environment.

The present disclosure provides video processing methods, apparatuses and systems. The method includes: obtaining a to-be-processed video, where the to-be-processed video is obtained by performing feature removal processing for one or more objects in an original video; obtaining a feature restoration processing request for one or more to-be-processed objects; according to the feature restoration processing request for the one or more to-be-processed objects, obtaining feature image information corresponding to the one or more to-be-processed objects, where the feature image information for one of the one or more to-be-processed objects includes pixel position information of all or part of features for the one of the one or more to-be-processed objects in the original video; according to the feature image information for the one or more to-be-processed objects, performing feature restoration processing for the one or more to-be-processed objects in the to-be-processed video.

The present disclosure provides video processing methods, apparatuses and systems. The method includes: obtaining a to-be-processed video, where the to-be-processed video is obtained by performing feature removal processing for one or more objects in an original video; obtaining a feature restoration processing request for one or more to-be-processed objects; according to the feature restoration processing request for the one or more to-be-processed objects, obtaining feature image information corresponding to the one or more to-be-processed objects, where the feature image information for one of the one or more to-be-processed objects includes pixel position information of all or part of features for the one of the one or more to-be-processed objects in the original video; according to the feature image information for the one or more to-be-processed objects, performing feature restoration processing for the one or more to-be-processed objects in the to-be-processed video.

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.

An information processing device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: selecting a base image from a base data set that is a set of images including a target region that includes an object that is a target of machine learning and a background region that does not include an object that is a target of the machine learning; generating a processing target image that is a duplicate of the selected base image; selecting the target region included in another image included in the base data set; synthesizing an image of the selected target region with the processing target image; and generating a data set that is a set of the processing target images in which a predetermined number of the target regions are synthesized.

The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

A method for automating an agricultural work task which is performed by a tillage device on an agricultural tractor includes modifying via a control unit at least one process control variable representing a working or operating parameter of the tillage device using feedback data which represent a field state of a field surface before or after tillage, generating via an imaging sensor a ground image of the field surface, and evaluating via a data processing unit the ground image to determine at least some of the feedback data. The data processing unit evaluates the ground image such that the ground image is used to determine the feedback data depending on the result of a monitoring of the field surface for visually covering air dust.

A method for automating an agricultural work task which is performed by a tillage device on an agricultural tractor includes modifying via a control unit at least one process control variable representing a working or operating parameter of the tillage device using feedback data which represent a field state of a field surface before or after tillage, generating via an imaging sensor a ground image of the field surface, and evaluating via a data processing unit the ground image to determine at least some of the feedback data. The data processing unit evaluates the ground image such that the ground image is used to determine the feedback data depending on the result of a monitoring of the field surface for visually covering air dust.

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.