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Publications about Markov Random Fields
Result of the query in the list of publications :
2 Articles |
1 - ant colony optimization for image regularization based on a non-stationary Markov modeling.
S. Le Hegarat-Mascle and A. Kallel and X. Descombes. IEEE Trans. on Image Processing, 16(3): pages 865-878, March 2007.
Keywords : Markov Random Fields, Ants colonization.
@ARTICLE{Ants07,
|
| author |
= |
{Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, |
| title |
= |
{ant colony optimization for image regularization based on a non-stationary Markov modeling}, |
| year |
= |
{2007}, |
| month |
= |
{March}, |
| journal |
= |
{IEEE Trans. on Image Processing}, |
| volume |
= |
{16}, |
| number |
= |
{3}, |
| pages |
= |
{865-878}, |
| keyword |
= |
{Markov Random Fields, Ants colonization} |
| } |
Abstract :
Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixed-form neighborhood used in classical Markov random field regularization techniques. The performance of this new approach is illustrated on a simulated image and on actual remote sensing images |
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2 - Spatio-temporal fMRI analysis using Markov Random Fields.
X. Descombes and F. Kruggel and Y. Von Cramon. IEEE Trans. Medical Imaging, 17(6): pages 1028-1039, 1998.
Note : to appear.
Keywords : fMRI, Markov Random Fields.
@ARTICLE{descombes98,
|
| author |
= |
{Descombes, X. and Kruggel, F. and Von Cramon, Y.}, |
| title |
= |
{Spatio-temporal fMRI analysis using Markov Random Fields}, |
| year |
= |
{1998}, |
| journal |
= |
{IEEE Trans. Medical Imaging}, |
| volume |
= |
{17}, |
| number |
= |
{6}, |
| pages |
= |
{1028-1039}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Xavier.Descombes/publis_dr/TMI1.pdf}, |
| keyword |
= |
{fMRI, Markov Random Fields} |
| } |
|
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5 Conference articles |
1 - Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features.
A. Voisin and G. Moser and V. Krylov and S.B. Serpico and J. Zerubia. In Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, Toulouse, France, September 2010.
Keywords : SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields.
Copyright : SPIE
@INPROCEEDINGS{7830-23,
|
| author |
= |
{Voisin, A. and Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, |
| volume |
= |
{7830}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00516333/en}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/51/63/33/PDF/Classification_of_VHR_SAR_SPIE_sept2010_Toulouse_Voisin.pdf}, |
| keyword |
= |
{SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields} |
| } |
Abstract :
This paper addresses the problem of the classification of very high resolution SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed images point out the accuracy of the proposed method, also as compared with previous contextual classifiers. |
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2 - A markov random field model for extracting near-circular shapes.
T. Blaskovics and Z. Kato and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, November 2009.
Keywords : Segmentation, Markov Random Fields, Shape prior.
@INPROCEEDINGS{Blaskovics09,
|
| author |
= |
{Blaskovics, T. and Kato, Z. and Jermyn, I. H.}, |
| title |
= |
{A markov random field model for extracting near-circular shapes}, |
| year |
= |
{2009}, |
| month |
= |
{November}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Cairo, Egypt}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5413472}, |
| keyword |
= |
{Segmentation, Markov Random Fields, Shape prior} |
| } |
|
3 - Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering.
R. Gaetano and G. Scarpa and G. Poggi and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 2008.
Keywords : Segmentation, Markov Random Fields, Mean Shift, Land Classification.
@INPROCEEDINGS{Gaetano2008,
|
| author |
= |
{Gaetano, R. and Scarpa, G. and Poggi, G. and Zerubia, J.}, |
| title |
= |
{Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering}, |
| year |
= |
{2008}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Lausanne, Switzerland}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7080521}, |
| keyword |
= |
{Segmentation, Markov Random Fields, Mean Shift, Land Classification} |
| } |
Abstract :
Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF.
We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node (thus allowing for a non-binary tree), and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method. |
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4 - Vers une détection et une classification non-supervisées des changements inter-images.
A. Fournier and X. Descombes and J. Zerubia. In Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA), 2007.
Keywords : Markov Random Fields, Registration, Change detection, Clustering.
@INPROCEEDINGS{fournier-taima-07,
|
| author |
= |
{Fournier, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Vers une détection et une classification non-supervisées des changements inter-images}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Proc. Traitement et Analyse de l'Information - Méthodes et Applications (TAIMA)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_fournier-taima-07.pdf}, |
| keyword |
= |
{Markov Random Fields, Registration, Change detection, Clustering} |
| } |
|
5 - Use of ant colony
optimization for finding neighbourhoods in non-stationary Markov random
field models.
S. Le Hegarat-Mascle and A. Kallel and X. Descombes. In Pattern Recognition and Machine Intelligence (PReMI'07), 2007.
Keywords : Ants colonization, Markov Random Fields.
@INPROCEEDINGS{ant07b,
|
| author |
= |
{Le Hegarat-Mascle, S. and Kallel, A. and Descombes, X.}, |
| title |
= |
{Use of ant colony
optimization for finding neighbourhoods in non-stationary Markov random
field models}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Pattern Recognition and Machine Intelligence (PReMI'07)}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_ant07b.pdf}, |
| keyword |
= |
{Ants colonization, Markov Random Fields} |
| } |
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