2007
Conference article  Restricted

Markov Zinciri Monte Carlo ile Tam Bayesçi Imge Ayrıstırma (Fully bayesian image separation using Markov chain Monte Carlo)

Kayabol K., Kuruoglu E. E., Sankur B.

Bayesian source separation  Management science  Event detection  Rare events  MCMC  Particle Filtering  Gibbs sampling  Markov random fields  Signal filtering and prediction  Monte Carlo methods  Signal processing  On-line detection  Mathematical models  Sequential Monte Carlo methods  Numerical experiments  Argon  State space methods  State spaces 

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.

Source: IEEE 15th Signal Processing and Communication Applications Conference, pp. 969–972, Eskisehir, Turkey, 11-13 June 2007

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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:91654,
	title = {Markov Zinciri Monte Carlo ile Tam Bayesçi Imge Ayr\ıst\ırma (Fully bayesian image separation using Markov chain Monte Carlo)},
	author = {Kayabol K. and Kuruoglu E.  E. and Sankur B.},
	doi = {10.1109/siu.2007.4298796},
	booktitle = {IEEE 15th Signal Processing and Communication Applications Conference, pp. 969–972, Eskisehir, Turkey, 11-13 June 2007},
	year = {2007}
}

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