Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-class Markovian segmentation of high-resolution sonar images
Computer Vision and Image Understanding
Pattern Recognition Letters
A Statistical Model for Contours in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Multisensor triplet Markov fields and theory of evidence
Image and Vision Computing
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
Double Markov random fields and Bayesian image segmentation
IEEE Transactions on Signal Processing
Estimation of generalized mixtures and its application in image segmentation
IEEE Transactions on Image Processing
Multiscale MAP filtering of SAR images
IEEE Transactions on Image Processing
Image Resolution Enhancement with Hierarchical Hidden Fields
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Modeling and Estimation of the Dynamics of Planar Algebraic Curves via Riccati Equations
Journal of Mathematical Imaging and Vision
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
Pattern Recognition Letters
International Journal of Computer Vision
Road surface marking classification based on a hierarchical markov model
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
Image segmentation using local spectral histograms and linear regression
Pattern Recognition Letters
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Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, a host of models have been proposed and the ones which have probably received considerable attention are the hidden Markov fields (HMF) models. This is due to their simplicity of handling and their potential for providing improved image quality. Although these models provide satisfying results in the stationary case, they can fail in the nonstationary one. In this paper, we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, which enables one to deal with nonstationary class fields. Moreover, the noise can be correlated and possibly non-Gaussian. An original parameter estimation method which uses the Pearson system to find the natures of the noise margins, which can vary with the class, is also proposed and used to perform unsupervised segmentation of such images. Experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones.