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Bayesian Image Segmentation Using MRF’s Combined with Hierarchical Prior Models

  • Kohta Aoki
  • Hiroshi Nagahashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

The problem of image segmentation can be formulated in the framework of Bayesian statistics. We use a Markov random field as the prior model of the spacial relationship between image pixels, and approximate an observed image by a Gaussian mixture model. In this paper, we introduce into the statistical model a hierarchical prior structure from which model parameters are regarded as drawn. This would give an efficient Gibbs sampler for exploring the joint posterior distribution of all parameters given an observed image and could make the estimation more robust.

Keywords

Mixture Model Posterior Distribution Markov Chain Monte Carlo Image Segmentation Gibbs Sampler 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kohta Aoki
    • 1
  • Hiroshi Nagahashi
    • 2
  1. 1.Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of Technology 
  2. 2.Imaging Science and Engineering LaboratoryTokyo Institute of TechnologyYokohamaJapan

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