Basketball Video Image Segmentation Using NeutrosophicFuzzy C-means Clustering Algorithm

Abstract

Basketball video image segmentation is important in image processing and computervision, which is significant for improving image quality and enhancing visual effects. However,traditional image segmentation algorithms still have challenges in dealing with noise and complexbackgrounds. This study presents research on basketball video image segmentation based onNeutrosophic fuzzy C-mean clustering algorithm. Firstly, the video image segmentation algorithmis studied and analyzed; secondly, to the computational time is reduced to get better segmentationresults by fuzzy C-mean clustering. The algorithm is carried out for basketball video imagesegmentation and compared and analyzed with the traditional segmentation algorithm. Resultsshowed that the peak SNR values were 14.96, 14.81, and 14.57 in pretzel noise environment. Thepeak SNR results were 13.97, 12.87, and 12.06 in Gaussian noise environment. The algorithm hasa significant advantage in both image segmentation performance. It improves the image qualityand visual effect, which is an important reference for future image analysis and processing.