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Spatial Sampling for Image Segmentation

  • Mariano Rivera
  • Oscar Dalmau
  • Washington Mio
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 62)

Abstract

We present a framework for image segmentation based on the ML estimator. A common hypothesis for explaining the differences among image regions is that they are generated by sampling different Likelihood Functions. We adopt last hypothesis and, additionally, we assume that such samples are i.i.d. Thus, the probability of a model generates the observed pixel value is estimated by computing the likelihood of the sample composed with the surrounding pixels.

Keywords

Image Segmentation Spatial Sampling Neighborhood Selection Bayesian Regularization Markov Random Field Modeling 
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 Science+Business Media B.V. 2011

Authors and Affiliations

  • Mariano Rivera
    • 1
  • Oscar Dalmau
    • 1
  • Washington Mio
    • 2
  1. 1.Centro de Investigacion en Matematicas A.CGuanajuatoMexico
  2. 2.Florida State UniversityTallahasseeUSA

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