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Adaptive anisotropic parameter estimation in the weak membrane model

  • Toshiro Kubota
  • Terry Huntsberger
Markov Random Fields
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1223)

Abstract

The weak membrane model uses Markov Random Fields within the Bayesian inference framework for image reconstruction and segmentation problems. Recently, the model has been extended for the 4D Gabor feature vector space and was applied to texture segmentation. A limitation of this technique is that the parameters in the model have to be adjusted for each different input image and they are fixed throughout the image. This paper proposes a technique to alleviate this limitation by estimating the parameters using local feature statistics. The technique has the following desirable properties: 1) the whole segmentation process is done in an unsupervised fashion, 2) robustness to noise and contrast variation, and 3) increased connectivity of boundaries.

Keywords

texture segmentation weak membrane model Bayesian inference Gaussian filters 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Toshiro Kubota
    • 1
  • Terry Huntsberger
    • 1
  1. 1.Intelligent Systems Laboratory, Department of Computer ScienceUniversity of South CarolinaColumbiaUSA

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