@inproceedings{e557ec214cb9472e9f9b60ec5e49b177,
title = "Evidential correlated gaussian mixture markov model for pixel labeling problem",
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.",
keywords = "Belief functions, Correlated noise model, Gaussian mixture, Image segmentation, Markov random field, Theory of evidence",
author = "Lin An and Ming Li and Boudaren, \{Mohamed El Yazid\} and Wojciech Pieczynski",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 4th International Conference on Belief Functions: Theory and Applications, BELIEF 2016 ; Conference date: 21-09-2016 Through 23-09-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-45559-4\_21",
language = "English",
isbn = "9783319455587",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "203--211",
editor = "Ji{\v r}ina Vejnarov{\'a} and V{\'a}clav Kratochv{\'i}l",
booktitle = "Belief Functions",
}