Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation
Fang Li 1, Michael K. Ng 2*1 Department of Mathematics, East China Normal University, Shanghai 200241, China.
2 Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
Received 16 June 2009; Accepted (in revised version) 31 December 2009
Available online 15 April 2010
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.AMS subject classifications: 62G86, 68U10, 94A08
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Key words: Texture, multiphase region competition, kernel density estimation, fuzzy membership function, total variation.
Email: firstname.lastname@example.org (F. Li), email@example.com (M. K. Ng)