Abstract
Hidden Markov Fields (HMF) have been widely used in various problems of image processing. In such models, the hidden process of interest \( X \) is assumed to be a Markov field that must be estimated from an observable process \( Y \). Classic HMFs have been recently extended to a very general model called “evidential pairwise Markov field” (EPMF). Extending its recent particular case able to deal with non-Gaussian noise, we propose an original variant able to deal with non-Gaussian and correlated noise. Experiments conducted on simulated and real data show the interest of the new approach in an unsupervised context.
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An, L., Li, M., Boudaren, M.E.Y., Pieczynski, W. (2016). Evidential Correlated Gaussian Mixture Markov Model for Pixel Labeling Problem. In: Vejnarová, J., Kratochvíl, V. (eds) Belief Functions: Theory and Applications. BELIEF 2016. Lecture Notes in Computer Science(), vol 9861. Springer, Cham. https://doi.org/10.1007/978-3-319-45559-4_21
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DOI: https://doi.org/10.1007/978-3-319-45559-4_21
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