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This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (mode of posterior marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data
Markov random fields, Multispectral imaging, Image segmentation, Fuzzy set theory, Density measurement, Context modeling, Covariance matrix, Extraterrestrial measurements, Bayesian methods, Robustness,missing data., Fuzzy Markov field, fuzzy Markov chain, parameterized joint density, multispectral image segmentation
"Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1753-1767, November 2006, doi:10.1109/TPAMI.2006.228
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