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Publications de Marc Berthod
Résultat de la recherche dans la liste des publications :
3 Articles |
1 - Extension of phase correlation to subpixel registration.
H. Foroosh et J. Zerubia et M. Berthod. IEEE Trans. on Image Processing, 11(3): pages 188 - 200, mars 2002.
@ARTICLE{forooshjzmb,
|
| author |
= |
{Foroosh, H. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Extension of phase correlation to subpixel registration}, |
| year |
= |
{2002}, |
| month |
= |
{mars}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{11}, |
| number |
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{3}, |
| pages |
= |
{188 - 200}, |
| pdf |
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{http://ieeexplore.ieee.org/iel5/83/21305/00988953.pdf?tp=&arnumber=988953&isnumber=21305}, |
| keyword |
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{} |
| } |
|
2 - Estimation of Markov Random Field prior parameters using Markov chain Monte Carlo Maximum Likelihood.
X. Descombes et R. Morris et J. Zerubia et M. Berthod. IEEE Trans. Image Processing, 8(7): pages 954-963, juillet 1999.
Mots-clés : 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 |
= |
{juillet}, |
| 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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3 - Unsupervised parallel image classification using Markovian models.
Z. Kato et J. Zerubia et M. Berthod. Pattern Recognition, 32(4): pages 591-604, 1999.
Mots-clés : Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification.
@ARTICLE{jz99a,
|
| author |
= |
{Kato, Z. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Unsupervised parallel image classification using Markovian models}, |
| year |
= |
{1999}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{32}, |
| number |
= |
{4}, |
| pages |
= |
{591-604}, |
| pdf |
= |
{http://dx.doi.org/10.1016/S0031-3203(98)00104-6}, |
| keyword |
= |
{Markov random field model, Hierarchical model, Parameter estimation, Parallel unsupervised image classification} |
| } |
Abstract :
This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation–maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images. |
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2 Articles de conférence |
1 - A step toward high resolution 3D SAR.
B. Pairault et M. Berthod. Dans Proc. European Conference on Synthetic Aperture Radar, Friedrichshafen, Germany, mai 1998.
@INPROCEEDINGS{berthod98,
|
| author |
= |
{Pairault, B. and Berthod, M.}, |
| title |
= |
{A step toward high resolution 3D SAR}, |
| year |
= |
{1998}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. European Conference on Synthetic Aperture Radar}, |
| address |
= |
{Friedrichshafen, Germany}, |
| keyword |
= |
{} |
| } |
|
2 - Denoising by extracting fractional order singularities.
H. Shekarforoush et J. Zerubia et M. Berthod. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seattle, USA, mai 1998.
@INPROCEEDINGS{jz98a,
|
| author |
= |
{Shekarforoush, H. and Zerubia, J. and Berthod, M.}, |
| title |
= |
{Denoising by extracting fractional order singularities}, |
| year |
= |
{1998}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
= |
{Seattle, USA}, |
| keyword |
= |
{} |
| } |
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