Disclosed is a system with motion capture function applied to ice hockey pass-and-control training, including a running part, a detection part and a display part. The detection part and the display part are in communication connection. The running part is ice hockey device with identifying characteristics, and the running part and the detection part are set to cooperate correspondingly; the detection part is used to detect the running part and capture the movement of the running part to form a detection signal and transmit it to the display part; the display part is used to store the virtual scene, receive the detection signal, and display part of the detection signal in the virtual scene for display. The system with motion capture function applied to ice hockey pass-and-control training of the disclosure has the advantages of low manufacturing cost, free of site restrictions, and realization of hand-eye separation of training methods.

A method for extracting a 3D object from a 3D environment, including segmenting an initial 2D scene obtained from an initial viewpoint to identify a plurality of object instances; mapping a selected object instance to an initial subset of triangles underlying the selected object instance; generating additional viewpoints around the selected object instance, and for each additional viewpoint: generating a further 2D scene from the additional viewpoint; segmenting the further 2D scene, thereby identifying candidate object instances; identifying a given candidate object instance as best matching the selected object instance, thereby obtaining a best matching candidate object instance; identifying an additional subset of triangles underlying the best matching candidate object instance; aggregating the additional subset of triangles to the initial subset of triangles; and outputting the aggregated subset of the triangles.

A method for extracting a 3D object from a 3D environment, including segmenting an initial 2D scene obtained from an initial viewpoint to identify a plurality of object instances; mapping a selected object instance to an initial subset of triangles underlying the selected object instance; generating additional viewpoints around the selected object instance, and for each additional viewpoint: generating a further 2D scene from the additional viewpoint; segmenting the further 2D scene, thereby identifying candidate object instances; identifying a given candidate object instance as best matching the selected object instance, thereby obtaining a best matching candidate object instance; identifying an additional subset of triangles underlying the best matching candidate object instance; aggregating the additional subset of triangles to the initial subset of triangles; and outputting the aggregated subset of the triangles.

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.

Provided are a device for a digital assay of targets according to an exemplary embodiment of the present disclosure and a method using the same. The digital assay method of targets according to the exemplary embodiment of the present disclosure includes acquiring an image for a plurality of microdroplets, predicting at least one region based on the image for the plurality of microdroplets using an artificial neural network-based prediction model configured to segment at least one region among positive microdroplets, negative microdroplets, and atypical microdroplets, with the image for the plurality of microdroplets as an input, determining a number for the plurality of microdroplets based on the at least one region, and providing quantitative data of targets based on the number for the plurality of microdroplets.

A method is provided for horizon detection. The method includes acquiring an image that depicts a view of an environment, and defining a line pattern of lines that divide the image into respective pairs of image segments. The line pattern is formed of lines that are parallel, or intersecting at a common point of intersection. The method includes searching the lines of the line pattern to identify one of the lines as an estimated true horizon in the image that divides the image into a respective pair of image segments at a boundary of greatest difference in average brightness between the image segments from among the respective pairs of image segments. The method includes determining true horizon in the image from the estimated true horizon. The method may also include an evaluation of the estimated true horizon or the true horizon as to verify one or more expected characteristics.

A circular sign candidate extraction device includes: a memory; and a processor coupled to the memory, the processor being configured to perform processing, the processing including: detecting a circle from a captured image; specifying an annular region surrounded by the detected circle and a concentric circle, the concentric circle being a circle having a radius different from the detected circle; setting one or more pixels among pixels included in the annular region as determination pixels; and extracting a circular sign candidate from the detected circle in accordance with comparison between a color of the determination pixel and a predetermined color.

The present invention discloses a method for updating road signs and markings on the basis of monocular images, comprising the following steps: acquiring street images of urban roads and GPS phase center coordinates and spatial attitude data corresponding to the street images; extracting coordinates of the road sign marking images; constructing a sparse three-dimensional model, and then generating a streetscape image depth map; calculating the space position of the road sign and marking according to the semantic and depth values of the image, the collinear equation and the space distance relation; if the same road sign and marking is visible in multiple views, solving the position information of the road sign; and vectorizing the obtained road sign position information, and fusing the information into the original data to realize the updating of the road sign data.

This application provides a method and apparatus for detecting a lane line in the field of artificial intelligence. One example method includes: scanning a surrounding environment of a vehicle by using a LIDAR, to obtain lane line candidate reflection points and road edge information; establishing a road edge coordinate system based on the road edge information; extracting lane line reflection points from the lane line candidate reflection points based on coordinates of the lane line candidate reflection points in the road edge coordinate system; and obtaining a lane line based on the lane line reflection points.