Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
A Hierarchical Self-organizing Associative Memory for Machine Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A note on the discrete binary Mumford-Shah model
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Fast algorithm of the ML estimator for passive synthetic arrays
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
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Markov chain Monte Carlo (MCMC) sampling methods have gained much popularity among researchers in signal processing. The Gibbs and the Metropolis-Hastings (1954, 1970) algorithms, which are the two most popular MCMC methods, have already been employed in resolving a wide variety of signal processing problems. A drawback of these algorithms is that in general, they cannot guarantee that the samples are drawn exactly from a target distribution. New Markov chain-based methods have been proposed, and they produce samples that are guaranteed to come from the desired distribution. They are referred to as perfect samplers. We review some of them, with the emphasis being given to the algorithm coupling from the past (CFTP). We also provide two signal processing examples where we apply perfect sampling. In the first, we use perfect sampling for restoration of binary images and, in the second, for multiuser detection of CDMA signals