Kernel density estimation basedmultiphase fuzzy region competition method for texture image segmentation

Fang Li, Kwok Po NG

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)623-641
Number of pages19
JournalCommunications in Computational Physics
Volume8
Issue number3
DOIs
Publication statusPublished - Sep 2010

Scopus Subject Areas

  • Physics and Astronomy (miscellaneous)

User-Defined Keywords

  • Fuzzy membership function
  • Kernel density estimation
  • Multiphase region competition
  • Texture
  • Total variation

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