This application provides an artificial intelligence-based pathological image processing method performed by an electronic device. The method includes: determining a seed pixel of an immune cell region from a pathological image; obtaining a seed pixel mask image corresponding to the seed pixel of the immune cell region from the pathological image based on the seed pixel of the immune cell region; segmenting an epithelial cell region in the pathological image, to obtain an epithelial cell mask image of the pathological image; fusing the seed pixel mask image and the epithelial cell mask image of the pathological image, to obtain an effective seed pixel mask image corresponding to the immune cell region in the pathological image; and determining a ratio value of the immune cell region in the pathological image based on the effective seed pixel mask image.

A system includes one or more video capture devices and a processor coupled to each video capture device. Each processor is operable to direct its respective video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, each bounding perimeter virtual object surrounding at least part of its respective object of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for multiple objects of interest, and may optionally generate an alert at least partially in response to the near-miss condition.

A method assess the quality of varnished wood surfaces. The method includes the following steps: a) creating a brightness map of the surface, b) creating a curvature map of the surface, c) ascertaining a cross-correlation of brightness map and curvature map, and d) evaluating the result of the cross-correlation to ascertain the proportion of the irregularities of the surface to be attributed to varnish flaws.

An image edge detection method for processing an image including multiple pixels includes performing a convolution operation on the image by a gradient operator in a first direction and a gradient operator in a second direction to obtain a first-direction gradient data and a second-direction gradient data, wherein the first direction is perpendicular to the second direction, performing a gradient statistical calculation within a neighboring area of a target pixel of the image according to the first-direction gradient data and the second-direction gradient data to obtain a gradient statistic, and determining an edge significance corresponding to the target pixel according to the gradient statistic.

The present embodiment provides an image restoration method and apparatus which generate independent different restoration models by performing learning for each of different resolutions, receive a distorted image, and apply a restoration model corresponding to the resolution of the distorted image among the independent different restoration models to restore the distorted image into an improved upscaled image centering on a restoration target object within the distorted image.

Various methods and systems are provided for real-time image segmentation of medical image data. In one example, the real-time image segmentation of the medical image data may include updating an initial segmentation of the medical image data in real-time. The update may be based on a user input to a regularization brush applied to the medical image data, the user input to the regularization brush allowing modification of a volume of the initial segmentation.

A method includes receiving a first image, receiving a motion dataset, determining a motion level, determining an initialization state, and determining a tracking level. In a first condition, the method includes generating a first image pyramid, detecting a plurality of features in the first image pyramid using a first detector threshold, and generating a first set of detected keypoints from the plurality of features. In a second condition, the method includes generating a second image pyramid, detecting the plurality of features in the second image pyramid using a second detector threshold, the second detector threshold being less restrictive than the first detector threshold, and generating a second set of detected keypoints. In a third condition, the method includes detecting the plurality of features in the first image according to the first detector threshold and generating a third set of detected keypoint.

A computer-implemented system and method for predicting female sex bovine offspring to result from a bovine embryo by processing video image data of the embryo. The method includes receiving image data derived from video of a target embryo taken at substantially real-time frame speed during an embryo observation period of time. The video contains recorded morphokinetic movement of the target embryo occurring during the embryo observation period of time. The movement is represented in the received image data and the received image data is processed using a model generated utilizing machine learning and correlated embryo outcome data.

A lane extraction method uses projection transformation of a 3D point cloud map, by which the amount of operations required to extract the coordinates of a lane is reduced by performing deep learning and lane extraction in a two-dimensional (2D) domain, and therefore, lane information is obtained in real time. In addition, black-and-white brightness, which is most important information for lane extraction on an image, is substituted by the reflection intensity of a light detection and ranging (LiDAR) sensor so that a deep learning model capable of accurately extracting a lane is provided. Therefore, reliability and competitiveness is enhanced in the field of autonomous driving, the field of road recognition, the field of lane recognition, and the field of HD road maps for autonomous driving, and the fields similar or related thereto, and more particularly, in the fields of road recognition and autonomous driving using LiDAR.

A method for automatically generating a Region Of Interest (ROI) centric image in an electronic device is provided. The method includes receiving an image frame(s), where the image frame(s) includes a plurality of objects. Further, the method includes identifying a first ROI, a second ROI, and a non-ROI in the image frame(s). Further, the method includes rescaling the second ROI in the image frame(s), summarizing the non-ROI in the image frame(s), and automatically generating the ROI centric image, where the ROI centric image includes the rescaled-first ROI, the rescaled-second ROI, the rescaled-non-ROI, and the summarized non-ROI.