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Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation

Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation
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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

Keywords:   

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