@Article{CiCP-8-3,
author = {},
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 = {<p style="text-align: justify;">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.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.160609.311209a},
url = {https://global-sci.com/article/81045/kernel-density-estimation-based-multiphase-fuzzy-region-competition-method-for-texture-image-segmentation}
}