Automated techniques may be used to process three-dimensional image sets obtained via computer tomography (CT), magnetic resonance imaging (MRI), and other techniques. Image data representing an anatomical feature (e.g., an aorta) may be automatically segmented to obtain mask data for the anatomical feature. The mask data may undergo automated centerline regression to obtain centerline data for the anatomical feature. Automated curved planar reformatting, using the centerline data, may be applied to the original image data and/or the mask data. The curved planar reformation results may be subjected to automated segmentation. The resulting image data set may be used for such purposes as visualization, disease detection, measurement of feature, and/or tracking the anatomical feature over time.

An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

Systems and methods are disclosed, including a non-transitory computer readable medium storing computer executable instructions that when executed by a processor cause the processor to identify a first image, a second image, and a third image, the first image overlapping the second image and the third image, the second image overlapping the third image; determine a first connectivity between the first image and the second image; determine a second connectivity between the first image and the third image; determine a third connectivity between the second image and the third image, the second connectivity being less than the first connectivity, the third connectivity being greater than the second connectivity; assign the first image, the second image, and the third image to a cluster based on the first connectivity and the third connectivity; conduct a bundle adjustment process on the cluster of the first image, the second image, and the third image.

A method and a device for identifying road markings, and a monocular camera is provided. The method includes: acquiring an original image captured by a monocular camera on a vehicle; determining a middle region where an object is located and left and right regions on both sides of the middle region from the original image; acquiring an image intensity distribution along a vertical direction of the original image in the left region, the right region and the middle region; and determining whether the object is a road marking by comparing an image intensity of the middle region relative to image intensities of the left region or the right region. The present application can achieve the object of accurately identifying road markings.

An edge detection method, an edge detection device, an electronic apparatus, and a storage medium are provided. The method includes: processing an input image to obtain a line drawing of grayscale contours, where the input image includes an object with edges, and the line drawing includes lines; merging the lines to obtain reference boundary lines; processing the input image to obtain boundary regions corresponding to the object; for each of the reference boundary lines, comparing the reference boundary line with the boundary regions, calculating a number of pixels on the reference boundary line belonging to the boundary regions to serve as a score of the reference boundary line to determine a plurality of scores corresponding to the reference boundary lines one-to-one; determining target boundary lines according to the reference boundary lines, the scores, and the boundary regions; and determining edges of the object according to the target boundary lines.

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.

The present invention discloses fiducial marker systems or tag systems and methods to detect and decode a tag. In one aspect, a tag comprises four corners. Two upper corners are interconnected to form a detection area. Two lower corners are interconnected to form another detection area. The detection areas are interconnected by a path. The path divides the space between the detection areas into two coding areas. In another aspect, a tag comprises four corners. The four corners are interconnected by multiple paths. The multiple paths divide the space defined by the four corners into multiple coding areas.

Segmentation of multi-energy CT data, including data in three or more energy bands. A user is enabled to input one or more region indicators in displayed CT data. Probability maps are generated and may be refined using distance metrics, which may include geodesic and Euclidean distance metrics. Segmentation may be based on the probability maps and/or refined probability maps. Segmentation of medical image data is also disclosed.

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.

This document describes systems and techniques for clustering track pairs for multi-sensor track association. Many track-association algorithms use pattern-matching processes that can be computationally complex. Clustering tracks derived from different sensors present on a vehicle may reduce the computational complexity by reducing the pattern-matching problem into groups of subproblems. The weakest connection between two sets of tracks is identified based on both the perspective from each track derived from a first sensor and the perspective of each track derived from a second sensor. By identifying and pruning the weakest connection between two sets of tracks, a large cluster of tracks may be split into smaller clusters. The smaller clusters may require fewer computations by limiting the quantity of candidate track pairs to be evaluated. Fewer computations result in processing the sensor information more efficiently that, in turn, may increase the safety and reliability of an automobile.