Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Evidence Theory and Its Applications
Evidence Theory and Its Applications
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 chains and theory of evidence
International Journal of Approximate Reasoning
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
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
Multisensor fusion in the frame of evidence theory for landmines detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of generalized mixture in the case of correlated sensors
IEEE Transactions on Image Processing
Efficient detection in hyperspectral imagery
IEEE Transactions on Image Processing
Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery
IEEE Transactions on Image Processing
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
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Hidden Markov Fields (HMF) are widely applicable to various problems of image processing. In such models, the hidden process of interest X is a Markov field, which must be estimated from its observable noisy version Y. The success of HMF is due mainly to the fact that X remains Markov conditionally on the observed process, which facilitates different processing strategies such as Bayesian segmentation. Such models have been recently generalized to 'Pairwise' Markov fields (PMF), which offer similar processing advantages and superior modeling capabilities. In this generalization, one directly assumes the Markovianity of the pair (X,Y). Afterwards, 'Triplet' Markov fields (TMF) have been proposed, in which the distribution of (X,Y) is the marginal distribution of a Markov field (X,U,Y), where U is an auxiliary random field. So U can have different interpretations and, when the set of its values is not too complex, X can still be estimated from Y. The aim of this paper is to show some connections between TMF and the Dempster-Shafer theory of evidence. It is shown that TMF allow one to perform the Dempster-Shafer fusion in different general situations, possibly involving several sensors. As a consequence, Bayesian segmentation strategies remain applicable.