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 algorithmis 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.
Scopus Subject Areas
- Physics and Astronomy (miscellaneous)
- Fuzzy membership function
- Kernel density estimation
- Multiphase region competition
- Total variation