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Publications about Markov processes
Result of the query in the list of publications :
Article |
1 - Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood.
X. Descombes and R. Morris and J. Zerubia and M. Berthod. IEEE Trans. Image Processing, 8(7): pages 954-963, July 1999.
Keywords : Markov processes, Monte Carlo methods, Potts model, Image segmentation, Maximum likelihood estimation .
@ARTICLE{xd99c,
|
| author |
= |
{Descombes, X. and Morris, R. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood}, |
| year |
= |
{1999}, |
| month |
= |
{July}, |
| journal |
= |
{IEEE Trans. Image Processing}, |
| volume |
= |
{8}, |
| number |
= |
{7}, |
| pages |
= |
{954-963}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=16772&arnumber=772239&count=14&index=6}, |
| keyword |
= |
{Markov processes, Monte Carlo methods, Potts model, Image segmentation, Maximum likelihood estimation } |
| } |
Abstract :
Developments in statistics now allow maximum likelihood estimators for the parameters of Markov random fields (MRFs) to be constructed. We detail the theory required, and present an algorithm that is easily implemented and practical in terms of computation time. We demonstrate this algorithm on three MRF models-the standard Potts model, an inhomogeneous variation of the Potts model, and a long-range interaction model, better adapted to modeling real-world images. We estimate the parameters from a synthetic and a real image, and then resynthesize the models to demonstrate which features of the image have been captured by the model. Segmentations are computed based on the estimated parameters and conclusions drawn. |
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Conference article |
1 - Fully Bayesian image segmentation-an engineering perspective.
R. Morris and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vol. 3, pages 54-57, Santa Barbara, CA, USA, October 1997.
Keywords : Bayes methods, Markov processes, Monte Carlo methods, Image sampling, Image segmentation.
@INPROCEEDINGS{MorrisICIP97,
|
| author |
= |
{Morris, R. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{ Fully Bayesian image segmentation-an engineering perspective}, |
| year |
= |
{1997}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| volume |
= |
{3}, |
| pages |
= |
{54-57}, |
| address |
= |
{Santa Barbara, CA, USA}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=631978&isnumber=13718}, |
| keyword |
= |
{Bayes methods, Markov processes, Monte Carlo methods, Image sampling, Image segmentation} |
| } |
Abstract :
Developments in Markov chain Monte Carlo procedures have made it possible to perform fully Bayesian image segmentation. By this we mean that all the parameters are treated identically, be they the segmentation labels, the class parameters or the Markov random field prior parameters. We perform the analysis by sampling from the posterior distribution of all the parameters. Sampling from the MRF parameters has traditionally been considered if not intractable then at least computationally prohibitive. In the statistics literature there are descriptions of experiments showing that the MRF parameters may be sampled by approximating the partition function. These experiments are all, however, on `toy' problems; for the typical size of image encountered in engineering applications the phase transition behaviour of the models becomes a major limiting factor in the estimation of the partition function. Nevertheless, we show that, with some care, fully Bayesian segmentation can be performed on realistic sized images. We also compare the fully Bayesian approach with the approximate pseudolikelihood method |
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