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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Besag, J.: On the statistical analysis of dirty pictures. J. Roy. Stat. Soc. Ser. B 48(3), 259–302 (1986)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)
Smets, P.: Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. Int. J. Approximate Reasoning 9, 1–35 (1993)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Bendjebbour, A., Delignon, Y., Fouque, L., Samson, V., Pieczynski, W.: Multisensor images segmentation using dempster-shafer fusion in Markov fields context. IEEE Trans. Geosci. Remote Sens. 39(8), 1789–1798 (2001)
Foucher, S., Germain, M., Boucher, J.-M., Benié, G.B.: Multisource classification using ICM and Dempster-Shafer theory. IEEE Trans. Instrum. Measur. 51(2), 277–281 (2002)
Le Hégarat-Mascle, S., Bloch, I., Vidal-Madjar, D.: Introduction of neighborhood information in evidence theory and application to data fusion of radar and optical images with partial cloud cover. Pattern Recogn. 31(11), 1811–1823 (1998)
Tupin, F., Maitre, H., Bloch, I.: A first step toward automatic interpretation of SAR images using evidential fusion of several structure detectors. IEEE Trans. Geosci. Remote Sens. 37(3), 1327–1343 (1999)
Pieczynski, W., Benboudjema, D.: Multisensor triplet Markov fields and theory of evidence. Image Vis. Comput. 24(1), 61–69 (2006)
Benboudjema, D., Pieczynski, W.: Unsupervised image segmentation using triplet Markov fields. Comput. Vis. Image Underst. 99(3), 476–498 (2005)
Boudaren, M.E.Y., An, L., Pieczynski, W.: Dempster-Shafer fusion of evidential pairwise Markov fields. Int. J. Approximate Reasoning 74, 13–29 (2016)
Poggi, G., Scarpa, G., Zerubia, J.B.: Supervised segmentation of remote sensing images based on a tree-structured MRF model. IEEE Trans. Geosci. Remote Sens. 43(8), 1901–1911 (2005)
Pieczynski, W., Tebbache, A.-N.: Pairwise Markov random fields and segmentation of textured images. Mach. Graph. Vis. 9, 705–718 (2000)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer Science & Business Media, Heidelberg (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-45559-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45558-7
Online ISBN: 978-3-319-45559-4
eBook Packages: Computer ScienceComputer Science (R0)