Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative Gibbsian technique for reconstruction of m-ary images
Pattern Recognition
Parameter estimation in hidden fuzzy Markov random fields and image segmentation
Graphical Models and Image Processing
MRF parameter estimation by an accelerated method
Pattern Recognition Letters
Pattern Recognition Letters
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Model for Contours in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Unsupervised restoration of hidden nonstationary Markov chains using evidential priors
IEEE Transactions on Signal Processing - Part II
An equivalence of the EM and ICE algorithm for exponential family
IEEE Transactions on Signal Processing
Double Markov random fields and Bayesian image segmentation
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Sonar image segmentation using an unsupervised hierarchical MRF model
IEEE Transactions on Image Processing
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Pattern Recognition Letters
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Hidden Markov fields (HMF) models are widely applied to various problems arising in image processing. In these models, the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y. The success of HMF is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian, which facilitates different processing strategies such as Bayesian restoration. HMF have been recently generalized to ''pairwise'' Markov fields (PMF), which offer similar processing advantages and superior modeling capabilities. In PMF one directly assumes the Markovianity of the pair (X,Y). Afterwards, ''triplet'' Markov fields (TMF), in which the distribution of the pair (X,Y) is the marginal distribution of a Markov field (X,U,Y), where U is an auxiliary process, have been proposed and still allow restoration processing. The aim of this paper is to propose a new parameter estimation method adapted to TMF, and to study the corresponding unsupervised image segmentation methods. The latter are validated via experiments and real image processing.