Fuzzy-based probabilistic relaxation for textured image segmentation

Chun Shien Lu, Pau Choo Chung

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)


This paper describes a Fuzzy-based Probabilistic Relaxation (FPR) for textured image segmentation. The FPR is developed based on an improvement of the conventional probabilistic relaxation which stops after the first few iterations even when the results are still far from satisfaction. The incapability of further improvement in the conventional probabilistic relaxation is detected by a proposed measure of fuzziness. In our FPR, probabilities in the relaxation are suitably adjusted/fuzzified based on a membership function to remove their crisp property such that further improvement can proceed. Experimental results indicate that the fuzzy-based probabilistic relaxation significantly improves the relaxation quality, especially for the textured images composed of components of significantly different sizes. Comparisons with conventional relaxation have also been conducted.

Original languageEnglish
Number of pages6
Publication statusPublished - 1994 Dec 1
EventProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA
Duration: 1994 Jun 261994 Jun 29


OtherProceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
CityOrlando, FL, USA

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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