In this paper, we propose a multi-phase 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.
Li et al. REF proposed a multiphase fuzzy region competition model for texture image segmentation.
Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation
Communications in Computational Physics
Mathematics