Entropy Controlled Gauss-Markov Random Measure Field Models for Early Vision

  • Mariano Rivera
  • Omar Ocegueda
  • Jose L. Marroquin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3752)


We present a computationally efficient segmentation–restoration method, based on a probabilistic formulation, for the joint estimation of the label map (segmentation) and the parameters of the feature generator models (restoration). Our algorithm computes an estimation of the posterior marginal probability distributions of the label field based on a Gauss Markov Random Measure Field model. Our proposal introduces an explicit entropy control for the estimated posterior marginals, therefore it improves the parameter estimation step. If the model parameters are given, our algorithm computes the posterior marginals as the global minimizers of a quadratic, linearly constrained energy function; therefore, one can compute very efficiently the optimal (Maximizer of the Posterior Marginals or MPM) estimator for multi–class segmentation problems. Moreover, a good estimation of the posterior marginals allows one to compute estimators different from the MPM for restoration problems, denoising and optical flow computation. Experiments demonstrate better performance over other state of the art segmentation approaches.


Markov Random Field Markov Chain Monte Carlo Method Early Vision Posterior Marginal Distribution Markov Random Field Model 
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

  • Mariano Rivera
    • 1
  • Omar Ocegueda
    • 1
  • Jose L. Marroquin
    • 1
  1. 1.Centro de Investigacion en Matematicas A.C.GuanajuatoMexico

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