Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation
Year: 2010
Communications in Computational Physics, Vol. 8 (2010), Iss. 3 : pp. 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.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.160609.311209a
Communications in Computational Physics, Vol. 8 (2010), Iss. 3 : pp. 623–641
Published online: 2010-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 19