Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

Abstract

National costume contains rich patterns and patterns with national characteristics, which is an important part of the study of national costume culture. Neither the traditional protection of national costumes nor the early digital protection can clearly and effectively preserve the details of national costumes, which is not conducive to the development and research of national costume culture. The development of image processing technology provides new technical support for the protection and inheritance of national costumes. This paper proposes the application of spatial neighborhood fuzzy c-means algorithm in 3D image segmentation of national clothing. Based on the traditional fuzzy c-means algorithm, combined with spatial information and gray information, a 3D image segmentation model based on spatial neighborhood fuzzy c-means is constructed. The experimental results show that the 3D image segmentation model based on spatial neighborhood fuzzy c-means has faster convergence speed and better algorithm performance than the improved fuzzy c-means algorithm and has better image segmentation effect in different noise levels. In the 3D image segmentation of national costumes, more detailed information can be retained on the basis of maintaining high accuracy and effect.

Retracted: Application of Spatial Neighborhood Fuzzy c-Means Algorithm in 3D Image Segmentation of National Clothing

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1].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:…”