Volume 8, Issue 3
Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation

Fang Li & Michael K. Ng

Commun. Comput. Phys., 8 (2010), pp. 623-641.

Published online: 2010-08

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  • 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 algorithm is 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.

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@Article{CiCP-8-623, author = {Fang Li and Michael K. Ng}, title = {Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation}, journal = {Communications in Computational Physics}, year = {2010}, volume = {8}, number = {3}, pages = {623--641}, 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 algorithm is 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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.160609.311209a}, url = {http://global-sci.org/intro/article_detail/cicp/7588.html} }
TY - JOUR T1 - Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation AU - Fang Li & Michael K. Ng JO - Communications in Computational Physics VL - 3 SP - 623 EP - 641 PY - 2010 DA - 2010/08 SN - 8 DO - http://dor.org/10.4208/cicp.160609.311209a UR - https://global-sci.org/intro/cicp/7588.html KW - AB -

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 algorithm is 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.

Fang Li & Michael K. Ng. (1970). Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation. Communications in Computational Physics. 8 (3). 623-641. doi:10.4208/cicp.160609.311209a
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