A Bayesian Segmentation Methodology for Parametric Image Models

Steven M.La Valle, Seth Andrew Hutchinson

Research output: Contribution to journalArticlepeer-review


Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context Our approach does not require parameter estimation and is therefore particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable. We apply this formulation to three distinct parametric model families that have been used in past segmentation schemes: implicit polynomial surfaces, parametric polynomial surfaces, and Gaussian Markov random fields. We present results on a variety of real range and intensity images.

Original languageEnglish (US)
Pages (from-to)211-217
Number of pages7
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number2
StatePublished - Feb 1995


  • Bayes factor
  • Bayesian methods
  • Markov random field
  • Statistical image segmentation
  • likelihoods
  • range images
  • texture segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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