Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation (Communications in Computational Physics, 2010)
In this paper, we propose a multiphase fuzzy region competition model fortexture image segmentation. In the functional, each region is represented by a fuzzymembership function and a probability density function that is estimated by a nonparametrickernel density estimation. The overall algorithm is very efficient as both thefuzzy membership function and the probability density function can be implementedeasily. We apply the proposed method to synthetic and natural texture images, andsynthetic aperture radar images. Our experimental results have shown that the proposedmethod is competitive with the other state-of-the-art segmentation methods.