Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional dependency measures
Journal of Multivariate Analysis
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
Signal and image segmentation using pairwise Markov chains
IEEE Transactions on Signal Processing
Estimation of generalized mixture in the case of correlated sensors
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
Fuzzy pairwise Markov chain to segment correlated noisy data
Signal Processing
Pearson-based mixture model for color object tracking
Machine Vision and Applications
Learning conditional random fields for classification of hyperspectral images
IEEE Transactions on Image Processing
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 the statistical restoration of hidden discrete signals, extending the classical methodology based on hidden Markov chains. The aim is to take into account the hidden signal and complex relationships between the noises which can be from different parametric models, non-independent, and of class-varying nature. We discuss some possibilities of managing it using copulas. Further, we propose a parameter estimation method and apply resulting unsupervised restoration methods in variety of situations. It is also validated by experiments performed in supervised and unsupervised context.