Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Coastline detection by a Markovian segmentation on SAR images
Signal Processing
Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Statistical methods for speech recognition
Statistical methods for speech recognition
Generalized Hilbert scan in image printing
Proceedings of the 6th Workshop on Theoretical Foundations of Computer Vision
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
Signal and image segmentation using pairwise Markov chains
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Baum's forward-backward algorithm revisited
Pattern Recognition Letters
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In this work, we propose to improve the neighboring relationship ability of the hidden Markov chain (HMC) model, by extending the memory lengths of both the Markov chain process and the data-driven densities arising in the model. The new model is able to learn more complex noise structures, with respect to the configuration of several previous states and observations. Model parameters estimation is performed from an extension of the general iterative conditional estimation (ICE) method to take into account memories, which makes the classification algorithm unsupervised. The higher-order HMC model is then evaluated in the image segmentation context. A comparative study conducted on a simulated image is carried out according to the order of the chain. Experimental results on a synthetic aperture radar (SAR) image show that higher-order model can provide more homogeneous segmentations than the classical model, but to the cost of higher memory and computing time requirements.