Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
An equivalence of the EM and ICE algorithm for exponential family
IEEE Transactions on Signal Processing
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Estimation of generalized mixture in the case of correlated sensors
IEEE Transactions on Image Processing
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
Sonar image segmentation using an unsupervised hierarchical MRF model
IEEE Transactions on Image Processing
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
IEEE Transactions on Image Processing
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
Kalman filtering in pairwise Markov trees
Signal Processing
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
Fuzzy pairwise Markov chain to segment correlated noisy data
Signal Processing
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
Fixed-Interval Kalman Smoothing Algorithms in Singular State---Space Systems
Journal of Signal Processing Systems
Unsupervised data classification using pairwise Markov chains with automatic copulas selection
Computational Statistics & Data Analysis
| Hi-index | 0.14 |
We propose a new model called a Pairwise Markov Chain (PMC), which generalizes the classical Hidden Markov Chain (HMC) model. The generalization, which allows one to model more complex situations, in particular implies that in PMC the hidden process is not necessarily a Markov process. However, PMC allows one to use the classical Bayesian restoration methods like Maximum APosteriori (MAP), or Maximal Posterior Mode (MPM). So, akin to HMC, PMC allows one to restore hidden stochastic processes, with numerous applications to signal and image processing, such as speech recognition, image segmentation, and symbol detection or classification, among others. Furthermore, we propose an original method of parameter estimation, which generalizes the classical Iterative Conditional Estimation (ICE) valid for of classical hidden Markov chain model, and whose extension to possibly non-Gaussian and correlated noise is briefly treated. Some preliminary experiments validate the interest of the new model.