期刊论文

中文

计算机工程与应用

1002-8331

2009

45

25

160-162

10.3778/j.issn.1002-8331.2009.25.049

长沙理工大学创新团队计划(Innovation Team Plan under Grant 2007CX05) 博士基金(Doctor Fund under Grant 1004154)

本校为其他机构

提出了一种结合熵和模糊C均值的聚类分割方法.模糊C均值(FCM)聚类算法广泛用于图像的自动分割,但是传统的FCM算法没有考虑像素的空间信息,因而对噪声十分敏感,基于二维直方图的模糊C均值聚类算法除了考虑像素点的灰度信息外还考虑了像素点邻域的空间信息,可有效地抑制噪声;在目标函数中引入熵项则能更好地抑制噪声和外围点对类中心估计的影响.实验分析结果表明,算法对湿地遥感图像的分割效果优于FCM算法.

A new effective multi-thresholds image segmentation method based on two-dimensional histogram FCM ＆ entropy clustering is presented.Fuzzy C-means clustering algorithm has been widely used in automated image segmentation.However,the conventional FCM algorithm is noise sensitive because of not taking account of the spatial information.Fuzzy C-means clustering algorithm based on two-dimensional histogram is robust for noise,because it utilizes the gray level information of each pixel and its spatial correlation information within the neighborhood...