Fuzzy c-means clustering based on spatial neighborhood information for image segmentation

Yanling Li; Yi Shen

首页> 中文期刊> 《系统工程与电子技术：英文版》 >Fuzzy c-means clustering based on spatial neighborhood information for image segmentation

Fuzzy c-means clustering based on spatial neighborhood information for image segmentation

开具论文收录证明 >>

期刊封面封底目录下载 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Fuzzy c-means(FCM) algorithm is one of the most popular methods for image segmentation.However,the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image.An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm.The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering.The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership function.It is not only effective to remove the noise spots but also can reduce the misclassified pixels.Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.

著录项

来源
《系统工程与电子技术：英文版》 |2010年第2期|323-328|共6页
作者
Yanling Li; Yi Shen;
展开▼
作者单位

Institute of Systems Engineering;

Department of Control Science and Engineering;

Huazhong University of Science and Technology;

Wuhan 430074;

P. R. China;

College of Computer and Information Technology;

Xinyang Normal University;

Xinyang 464000;

P. R. China;

展开▼
原文格式 PDF
正文语种 chi
中图分类测绘数据库与信息系统;
关键词
图像分割方法; 邻域信息; 均值聚类; 空间信息; 模糊; FCM算法; 抗噪性能; 空间聚类;

Fuzzy c-means clustering based on spatial neighborhood information for image segmentation

摘要

著录项

相关主题

期刊订阅