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Fast Segmentation of Markov Random Fields Corrupted by Correlated Noise

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Advances in Computing Systems and Applications (CSA 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 199))

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Abstract

Markov Random fields (MRF) represent a powerful mathematical model and they are used in several areas, but it is almost impossible to perform exact analytical calculations when using MRF and we must use approximations and iterative methods that are greedy in terms of time and computing resources. In the literature, proposed MRF methods for Bayesian and parameters estimation are complicated for implementation and represent many disadvantages in practice.

We propose in this work, and in order to remedy the problems mentioned above, a very simple MAP-MRF framework based mainly on local conditional probabilities, contrary to the existing solutions in literature where we rely on the energy function model.

Two powerful models based on the proposed framework are then presented. They will be compared with two recent works to show how they are more efficient with respect to classical models when it comes to the unsupervised segmentation of corrupted data with correlated noise.

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Author information

Authors and Affiliations

  1. Ecole Militaire Polytechnique, PO Box 17, Bordj El Bahri, 16111, Algiers, Algeria

    Ahmed Habbouchi & Mohamed El Yazid Boudaren

  2. Université des Sciences et de la Technologie Houari Boumediene, Algiers, Algeria

    Amar Aïssani

  3. Télécom SudParis, 9 rue Charles Fourier, 91011, Evry Cedex, France

    Wojciech Pieczynski

Authors
  1. Ahmed Habbouchi
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  2. Mohamed El Yazid Boudaren
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  3. Amar Aïssani
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  4. Wojciech Pieczynski
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Corresponding author

Correspondence to Ahmed Habbouchi .

Editor information

Editors and Affiliations

  1. Ecole Militaire Polytechnique, Algiers, Algeria

    Mustapha Reda Senouci

  2. Ecole Militaire Polytechnique, Algiers, Algeria

    Mohamed El Yazid Boudaren

  3. Ecole Militaire Polytechnique, Algiers, Algeria

    Faouzi Sebbak

  4. Ecole Militaire Polytechnique, Algiers, Algeria

    M'hamed Mataoui

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Habbouchi, A., Boudaren, M.E.Y., Aïssani, A., Pieczynski, W. (2021). Fast Segmentation of Markov Random Fields Corrupted by Correlated Noise. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_30

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