Spatial Classification Using Fuzzy Membership Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
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Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
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Signal Processing
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Pattern Recognition
Signal and image segmentation using pairwise Markov chains
IEEE Transactions on Signal Processing
Generalized hidden Markov models. I. Theoretical frameworks
IEEE Transactions on Fuzzy Systems
Baum's forward-backward algorithm revisited
Pattern Recognition Letters
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
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This paper deals with an image segmentation process using a new fuzzy Markov model, which characterizes the imprecision of the hidden data and the correlation of the observed data. We propose to extend a recent pairwise Markov chain model (PMC [W. Pieczynski, Pairwise Markov chains, IEEE Trans. Pattern Anal. Mach. Intell. 25 (5) (2003) 634-639]) to a fuzzy context, allowing us to treat a spatial correlated noise between neighboring observations. The new algorithm, called fuzzy pairwise Markov chain (FPMC), requires a more specific methodology in order to compute the posterior density related to the hidden field. We validate our approach through experiments performed on synthetic and real images.