PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Kayabol K., Kuruoglu E. E., Sankur B. Markov Zinciri Monte Carlo ile Tam Bayesçi Imge Ayrıstırma (Fully bayesian image separation using Markov chain Monte Carlo). In: IEEE 15th Signal Processing and Communication Applications Conference (Eskisehir, Turchia, 11-13 June 2007). Proceedings, vol. 1 pp. 969 - 972. IEEE Turkey Signal Processing Chapter (ed.). Anadolu University Press, 2007.
 
 
Abstract
(English)
In this study, we investigate the image separation problem under noisy environments. In the definition of the problem, the Bayesian approach is considered. We present a fully stochastic method based on Markov chain Monte Carlo (MCMC), instead of other deterministic methods, used in Bayesian image separation.
Abstract
(Italiano)
In turco: Bu calismada, gurultu altinda imge kaynaklarini ayirma problemi incelenmistir. Problemin ifade edilmesinde Bayesci yaklasima dayali yontemler uzerinde durulmustur. Bayesci imge kaynaklari ayirmada kullanilan gradyene dayali algoritmalarin yerine Markov zinciri Monte Carlo'ya (Markov Chain Monte Carlo: MCMC) dayanan tamamen istatistiksel bir ayristirma yontemi sunulmustur.
URL: http://siu2007.anadolu.edu.tr/en/index.php
DOI: 10.1109/SIU.2007.4298796
Subject Bayesian source separation
MCMC
Gibbs sampling
Markov random fields
G.3 Probability and Statistics. Probabilistic algorithms (including Monte Carlo)
I.4.4 Restoration
62C10 Bayesian problems; characterization of Bayes procedures
65C05 Monte Carlo methods


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