Various examples of the disclosure relate to aspects associated with training a machine-learned algorithm configured to count cells in a microscopy image or to determine a degree of confluence of the cells.

System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.

Methods and apparatus, including computer program products, are provided for machine learning including deep convolutional neural networks. In some example embodiments, there may be provided a method that includes receiving, at a trained machine learning model, a portion of a test image; activating, at the machine learning model, a convolutional result formed based on the portion of the test image, the activation based on neighboring regions in the test image; and providing, by the machine learning model, an activated output. Related systems, methods, and articles of manufacture are also described.

A method for restoring video data of a pipe based on computer vision is provided. The method includes: performing gray stretching on pipe image/video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm.

A method for restoring video data of a pipe based on computer vision is provided. The method includes: performing gray stretching on pipe image/video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm.

A system and a method for detecting welding based on edge computing, the system includes at least one edge server, each edge server configured to obtain welding information of at least one welding machine, preprocess the welding information to generate processed data, input the processed data to a trained algorithm to generate a detecting result, and determine a welding quality of the welding machine according to the detecting result; and a data server coupled to the at least one edge server and configured to process and store the detecting result and the welding information uploaded by each edge server, generate display information, and visualizes the detecting result according to the display information.

A system for forecasting when solar flares will occur and identifying their possible locations creates a mask to analyze an image of a solar disk. The system also creates an intensity histogram using the solar disk. Additionally, the system stores a resulting full-disk image and a sub-image identifying possible flare signature sites. Further, the system applies a method to filter out false positive detections by using an equation that enhances true positive pre-flare signatures and minimizes false positive signatures.

A system for forecasting when solar flares will occur and identifying their possible locations creates a mask to analyze an image of a solar disk. The system also creates an intensity histogram using the solar disk. Additionally, the system stores a resulting full-disk image and a sub-image identifying possible flare signature sites. Further, the system applies a method to filter out false positive detections by using an equation that enhances true positive pre-flare signatures and minimizes false positive signatures.

A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.