Research on Sports Video Image Based on Clustering Extraction

Abstract

Today, with the continuous sports events, the major sports events are also loved by the majority of the audience, so the analysis of the video data of the games has higher research value and application value. This paper takes the video of volleyball, tennis, baseball, and water polo as the research background and analyses the video images of these four sports events. Firstly, image graying, image denoising, and image binarization are used to preprocess the images of the four sports events. Secondly, feature points are used to detect the four sports events. According to the characteristics of these four sports events, SIFT algorithm is adopted to detect the good performance of SIFT feature points in feature matching. According to the simulation experiment, it can be seen that the SIFT algorithm can effectively detect football and have good anti-interference. For sports recognition, this document adopts the frame cross-sectional cumulative algorithm. Through simulation experiments, it can be seen that the grouping algorithm can achieve a recognition rate of more than 80% for sporting events, so it can be seen that the recognition algorithm is suitable for recognizing sports events videos.

A Data‐Driven Analysis of Myocardial Tissue Contraction on Mild Transverse Aortic Constriction

“…To elucidate the effects of mild TAC in the cardiomyocyte, we employed a data-driven approach to identify clusters of features that are effective in distinguishing between the pre-TAC and post-TAC mice. First, we binarized the all images [16,17] to remove the artifacts unrelated to the motion of myocardial tissue. Then, we extracted distinctive features from the images using speeded up robust features (SURF) algorithm [18], which extracts distinctive points between similar images while adjusting for image scaling and rotation.…”

Image Segmentation of the Continuous Action of Spiking in Volleyball Based on Spatial Neighborhood Information

“…Sports videos can intuitively record a series of scenes of athletes' continuous actions during sports, and contain an abundance of high-level semantic information [10][11][12][13][14]. In real scenes of sports competitions, interferences like light intensity and camera jitter make it difficult to accurately segment and identify the action information in the complex continuous actions of athletes [15][16][17][18][19].…”

Retracted: Research on Sports Video Image Based on Clustering Extraction

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”