Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Bayesian learning in undirected graphical models: approximate MCMC algorithms
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Image magnification based on a blockwise adaptive Markov random field model
Image and Vision Computing
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
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Markov random field (MRF) modelling is a popular method for pattern recognition and computer vision and MRF parameter estimation is of particular importance to MRF modelling. In this paper, a new approach based on Metropolis-Hastings algorithm and gradient method is presented to estimate MRF parameters. With properly chosen proposal distribution for Metropolis-Hastings algorithm, the Markov chain constructed by the method converges to stationary distribution quickly and it gives a good estimation result.