Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized hidden Markov models. I. Theoretical frameworks
IEEE Transactions on Fuzzy Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Baum's forward-backward algorithm revisited
Pattern Recognition Letters
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
Unsupervised data classification using pairwise Markov chains with automatic copulas selection
Computational Statistics & Data Analysis
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This paper deals with a recent statistical model based on fuzzy Markov random chains for image segmentation, in the context of stationary and non-stationary data. On one hand, fuzzy scheme takes into account discrete and continuous classes through the modeling of hidden data imprecision and on the other hand, Markovian Bayesian scheme models the uncertainty on the observed data. A non-stationary fuzzy Markov chain model is proposed in an unsupervised way, based on a recent Markov triplet approach. The method is compared with the stationary fuzzy Markovian chain model. Both stationary and non-stationary methods are enriched with a parameterized joint density, which governs the attractiveness of the neighbored states. Segmentation task is processed with Bayesian tools, such as the well known MPM (Mode of Posterior Marginals) criterion. To validate both models, we perform and compare the segmentation on synthetic images and raw optical patterns which present diffuse structures.