Basketball Video Image Segmentation Using NeutrosophicFuzzy C-means Clustering Algorithm

Abstract

Basketball video image segmentation is important in image processing and computer vision, which is significant for improving image quality and enhancing visual effects. However, traditional image segmentation algorithms still have challenges in dealing with noise and complex backgrounds. Research on basketball video image segmentation based on Neutrosophic fuzzy C-means clustering algorithm was conducted in this paper. Firstly, the video image segmentation algorithm was studied and analyzed. Secondly, the computational time was reduced to get better segmentation results by fuzzy C-mean clustering. The algorithm was carried out for basketball video image segmentation, which was compared and analyzed with the traditional segmentation algorithm. Results showed that the peak SNR values were 14.96, 14.81, and 14.57 in pretzel noise environment. The peak SNR results were 13.97, 12.87, and 12.06 in Gaussian noise environment. The algorithm has a significant advantage in both image segmentation performance. It improves the image quality and visual effect, which is an important reference for future image analysis and processing.Povzetek: Algoritem za segmentacijo video slik v košarki uporablja gručenje Neutrosophic fuzzy C-means, kar izboljša kakovost slike in vizualne učinke ter zmanjša čas izračuna v primerjavi s tradicionalnimi metodami.