Document Type
Journal Article
Department/Unit
Department of Mathematics
Title
Kernel density estimation based multiphase fuzzy region competition method for texture image segmentation
Language
English
Abstract
In this paper, we propose a multiphase fuzzy region competition model for texture image segmentation. In the functional, each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation. The overall algorithmis very efficient as both the fuzzy membership function and the probability density function can be implemented easily. We apply the proposed method to synthetic and natural texture images, and synthetic aperture radar images. Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods. © 2010 Global-Science Press.
Keywords
Fuzzy membership function, Kernel density estimation, Multiphase region competition, Texture, Total variation
Publication Date
2010
Source Publication Title
Communications in Computational Physics
Volume
8
Issue
3
Start Page
623
End Page
641
Publisher
Global Science Press
DOI
10.4208/cicp.160609.311209a
Link to Publisher's Edition
http://dx.doi.org/10.4208/cicp.160609.311209a
ISSN (print)
18152406
ISSN (electronic)
19917120
Recommended Citation
Li, Fang, and Michael K. Ng. "Kernel density estimation based multiphase fuzzy region competition method for texture image segmentation." Communications in Computational Physics 8.3 (2010): 623-641.