Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Dempster-Shafer Fusion of Dependent Sensors
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Computer Vision and Image Understanding
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
Analysis of evidence-theoretic decision rules for pattern classification
Pattern Recognition
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
Signal and image segmentation using pairwise Markov chains
IEEE Transactions on Signal Processing
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
International Journal of Approximate Reasoning
Extension of higher-order HMC modeling with application to image segmentation
Digital Signal Processing
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Multi-camera people tracking using evidential filters
International Journal of Approximate Reasoning
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Shape from silhouette using Dempster-Shafer theory
Pattern Recognition
Evidential Markov decision processes
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
Efficient Bayesian estimation of the multivariate Double Chain Markov Model
Statistics and Computing
Hi-index | 0.00 |
Hidden Markov chains (HMC) are widely applied in various problems occurring in different areas like Biosciences, Climatology, Communications, Ecology, Econometrics and Finances, Image or Signal processing. In such models, the hidden process of interest X is a Markov chain, which must be estimated from an observable Y, interpretable as being a noisy version of X. The success of HMC is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed process remains Markov, which makes possible different processing strategies such as Bayesian restoration. HMC have been recently generalized to ''Pairwise'' Markov chains (PMC) and ''Triplet'' Markov chains (TMC), which offer similar processing advantages and superior modeling capabilities. In PMC, one directly assumes the Markovianity of the pair (X,Y) and in TMC, the distribution of the pair (X,Y) is the marginal distribution of a Markov process (X,U,Y), where U is an auxiliary process, possibly contrived. Otherwise, the Dempster-Shafer fusion can offer interesting extensions of the calculation of the ''a posteriori'' distribution of the hidden data. The aim of this paper is to present different possibilities of using the Dempster-Shafer fusion in the context of different multisensor Markov models. We show that the posterior distribution remains calculable in different general situations and present some examples of their applications in remote sensing area.