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Adaptive anisotropic parameter estimation in the weak membrane model

  • Markov Random Fields
  • Conference paper
  • First Online:
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 1997)

Part of the book series: Lecture Notes in Computer Science ((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.

Research supported in part under ONR Grant No. N00014-94-1-1163 and ARO Grant No. DAAH04-96-10326.

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Authors and Affiliations

  1. Intelligent Systems Laboratory, Department of Computer Science, University of South Carolina, 29208, Columbia, SC, USA

    Toshiro Kubota & Terry Huntsberger

Authors
  1. Toshiro Kubota
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  2. Terry Huntsberger
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Editor information

Marcello Pelillo Edwin R. Hancock

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© 1997 Springer-Verlag Berlin Heidelberg

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Kubota, T., Huntsberger, T. (1997). Adaptive anisotropic parameter estimation in the weak membrane model. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_80

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  • DOI: https://doi.org/10.1007/3-540-62909-2_80

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62909-2

  • Online ISBN: 978-3-540-69042-9

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