Abstract
Hidden Markov models have been widely applied for data processing, mainly for image segmentation. However, when applied to non-stationary data, the results obtained may be poor due to mismatch between the models and reality. To this, such models have been extended to triplet Markov models which exhibit better performances while keeping similar computational complexity. In this paper, we introduce two novel models, based on triplet chain and field, able to handle simultaneously noise and classes non-stationarity, through a pairwise markovian auxiliary process. The performances of our models were assessed against regular hidden Markov models on simulated and synthetic images segmentation. The results obtained confirm the interest of the models proposed.
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References
Amat, F., Moussavi, F., Comolli, L.R., Elidan, G., Downing, K.H., Horowitz, M.: Markov random field based automatic image alignment for electron tomography. J. Struct. Biol. 161(3), 260–275 (2008). https://doi.org/10.1016/j.jsb.2007.07.007
Benboudjema, D., Pieczynski, W.: Unsupervised statistical segmentation of nonstationary images using triplet Markov fields. Pattern Anal. Mach. Intell. IEEE Trans. 29(8), 1367–1378 (2007)
Boudaren, M.E.Y., Pieczynski, W., Monfrini, E.: Unsupervised segmentation of non stationary data hidden with non stationary noise. In: International Workshop on Systems, Signal Processing and their Applications, WOSSPA, pp. 255–258 (2011)
Chen, M., Cho, J., Zhao, H.: Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics 7(4), e1001353 (2011). https://doi.org/10.1371/journal.pgen.1001353
Deng, H., Clausi, D.: Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model. IEEE Trans. Geosci. Remote Sens. 43(3), 528–538 (2005). https://doi.org/10.1109/tgrs.2004.839589
Habbouchi, A., Boudaren, M.E.Y., Aïssani, A., Pieczynski, W.: Fast segmentation of Markov random fields corrupted by correlated noise. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds.) CSA 2020. LNNS, vol. 199, pp. 334–343. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69418-0_30
Habbouchi, A., Boudaren, M.E.Y., Aïssani, A., Pieczynski, W.: Unsupervised segmentation of Markov random fields corrupted by nonstationary noise. IEEE Signal Process. Lett. 23(11), 1607–1611 (2016)
Hifny, Y., Renals, S.: Speech recognition using augmented conditional random fields. IEEE Trans. Audio Speech Lang. Process. 17(2), 354–365 (2009). https://doi.org/10.1109/tasl.2008.2010286
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, London (2010). https://doi.org/10.1007/978-1-84800-279-1
Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989). https://doi.org/10.1109/5.18626
Zhang, P., Li, B., Boudaren, M.E.Y., Yan, J., Li, M., Wu, Y.: Parameter estimation of generalized gamma distribution toward SAR image processing. IEEE Trans. Aerosp. Electron. Syst. 56(5), 3701–3717 (2020)
Zweig, G., Nguyen, P.: A segmental CRF approach to large vocabulary continuous speech recognition. In: 2009 IEEE Workshop on Automatic Speech Recognition & Understanding. IEEE, December 2009. https://doi.org/10.1109/asru.2009.5372916
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Habbouchi, A., Boudaren, M.E.Y., Senouci, M.R., Aïssani, A. (2022). Markovian Segmentation of Non-stationary Data Corrupted by Non-stationary Noise. In: Senouci, M.R., Boulahia, S.Y., Benatia, M.A. (eds) Advances in Computing Systems and Applications. CSA 2022. Lecture Notes in Networks and Systems, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-12097-8_3
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DOI: https://doi.org/10.1007/978-3-031-12097-8_3
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