Abstract
Aimed at the shortcomings of the current sports video image segmentation methods, such as rough image segmentation results and high spatial distortion rate, a sports video image segmentation method based on a fuzzy clustering algorithm is proposed. The second-order fuzzy attribute with normal distribution and gravity value is established by using the time-domain difference image, and the membership function of the fuzzy attribute is given; then, the time-domain difference image is fuzzy clustered, and the motion video image segmentation result is obtained by edge detection. Experimental results show that this method has high spatial accuracy, good noise iteration performance, and low spatial distortion rate and can accurately segment complex moving video images and obtain high-definition images. The application of this video image analysis method will help master the rules of sports technology and the characteristics of healthy people’s sports skills through video image analysis and help improve physical education, national fitness level, and competitive sports level.
Highlights
Web systems are substantially different from more conventional software systems
The accurate detection of the motion change region is the key of this kind of method, so the research of motion change region detection is of great significance
Image segmentation is an indispensable link in image technology and machine vision and is one of the bottlenecks in the development of image theory
Summary
Web systems are substantially different from more conventional software systems They are developed in shorter time frames and with smaller budgets, meet a more generic set of requirements, and generally serve a less specific user group [1]. We added QPop (the dimensionality reduction result of media content usage logs) as a content object for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase time segmentation, thereby improving mining performance. The video coding technology needs to segment the object image to ensure the integrity of the target object. The traditional video coding standard cannot search the content of the video It has a certain degree of difficulty in image segmentation.
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