TY - GEN
T1 - Fast Segmentation of Markov Random Fields Corrupted by Correlated Noise
AU - Habbouchi, Ahmed
AU - Boudaren, Mohamed El Yazid
AU - Aïssani, Amar
AU - Pieczynski, Wojciech
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - CRF)
KW - Correlated noise
KW - ICE
KW - ICM
KW - MPM
KW - Markov Random Field (MRF
KW - Parameters estimation
KW - Segmentation
U2 - 10.1007/978-3-030-69418-0_30
DO - 10.1007/978-3-030-69418-0_30
M3 - Conference contribution
AN - SCOPUS:85104449241
SN - 9783030694173
T3 - Lecture Notes in Networks and Systems
SP - 334
EP - 343
BT - Advances in Computing Systems and Applications - Proceedings of the 4th Conference on Computing Systems and Applications
A2 - Senouci, Mustapha Reda
A2 - Boudaren, Mohamed El
A2 - Sebbak, Faouzi
A2 - Mataoui, M'hamed
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th Conference on Computing Systems and Applications, CSA 2020
Y2 - 14 December 2020 through 14 December 2020
ER -