Segmentation of fuzzy images: A novel and fast two-step pseudo MAP method

Zhenzhou Wang, Yuming Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper presents a new two-step pseudo maximum a posteriori (MAP) segmentation method for the Markov random field (MRF)-modeled image because the exact MAP estimation is hard to implement due to intractable complexity. The expectation maximization (EM) and Markov Chain Monte Carlo (MCMC) methods are adopted to estimate the parameters for the MRF model due to their comparatively good performance. Although the image segmentation algorithms via graph cuts have become very popular nowadays, our proposed algorithm still performs significantly better in automatic identification and segmentation of fuzzy images than them, which is shown by the quantitative results on synthesized images. In practical applications, the proposed two-step pseudo MAP method is superior in segmenting the fuzzy laser images reflected from the weld pool surfaces during the P-GMAW welding process.

Original languageEnglish
Pages (from-to)1209-1218
Number of pages10
JournalMachine Vision and Applications
Issue number6
StatePublished - Nov 2012


  • EM
  • Fuzzy images
  • Graph cut
  • MAP
  • MCMC
  • Normalized graph cut
  • Welding

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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