DocumentCode :
432745
Title :
Efficient proposal distributions for MCMC image segmentation
Author :
Kostiainen, Timo ; Lampinen, Jouko
Author_Institution :
Lab. of Comput. Eng., Helsinki Univ. of Technol., Espoo, Finland
Volume :
2
fYear :
2004
fDate :
24-27 Oct. 2004
Firstpage :
933
Abstract :
We present methods to obtain computationally efficient proposal distributions for Bayesian reversible jump Markov chain Monte Carlo (RJMCMC) based image segmentation. The slow convergence of MCMC methods often makes them poorly suited for practical image processing applications. We show how carefully crafted proposal distributions along with certain approximations can decrease the computational cost of MCMC image segmentation to a level that is comparable with some traditional algorithms. We also discuss the interpretation of the resulting distribution of different segmentations and present experimental results.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; image segmentation; Bayesian method; RJMCMC; image processing application; image segmentation; proposal distribution; reversible jump Markov chain Monte Carlo method; Bayesian methods; Computational efficiency; Computer vision; Convergence; Distributed computing; Image segmentation; Markov random fields; Monte Carlo methods; Probability distribution; Proposals;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2004. ICIP '04. 2004 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-8554-3
Type :
conf
DOI :
10.1109/ICIP.2004.1419453
Filename :
1419453
Link To Document :
بازگشت