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Unsupervised image segmentation with Gaussian Pairwise Markov Fields

Author

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  • Gangloff, Hugo
  • Courbot, Jean-Baptiste
  • Monfrini, Emmanuel
  • Collet, Christophe

Abstract

Modeling strongly correlated random variables is a critical task in the context of latent variable models. A new probabilistic model, called Gaussian Pairwise Markov Field, is presented to generalize existing Markov Fields latent variables models, and to introduce more correlations between variables. This is done by considering the correlations within Gaussian Markov Random Fields models which are much richer than in the classical Markov Field models. The assets of the Gaussian Pairwise Markov Field model are explained. In particular, it offers a generalization of the classical Markov Field modelization that is highlighted. The new model is also considered in the practical case of unsupervised segmentation of images corrupted by long-range spatially-correlated noise, producing interesting new results.

Suggested Citation

  • Gangloff, Hugo & Courbot, Jean-Baptiste & Monfrini, Emmanuel & Collet, Christophe, 2021. "Unsupervised image segmentation with Gaussian Pairwise Markov Fields," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000128
    DOI: 10.1016/j.csda.2021.107178
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    References listed on IDEAS

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    1. Kleiber, William, 2016. "High resolution simulation of nonstationary Gaussian random fields," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 277-288.
    2. Yang Chen & Yinsheng Li & Hong Guo & Yining Hu & Limin Luo & Xindao Yin & Jianping Gu & Christine Toumoulin, 2012. "CT Metal Artifact Reduction Method Based on Improved Image Segmentation and Sinogram In-Painting," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-18, August.
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    Cited by:

    1. Gangloff, Hugo & Morales, Katherine & Petetin, Yohan, 2023. "Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).

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