Publications de 1999

Résultat de la recherche dans la liste des publications :

• 8 Articles
• 1 Thèse de Doctorat et Habilitation
• 14 Articles de conférence
• 3 Rapports de recherche et Rapports techniques

nouvelle recherche | bibtex

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8 Articles

1 - Comparison of Filtering Methods for fMRI Datasets. F. Kruggel et Y. Von Cramon et X. Descombes. NeuroImage, 10(5): pages 530-543, novembre 1999. @ARTICLE{xd99d, author = {Kruggel, F. and Von Cramon, Y. and Descombes, X.}, title = {Comparison of Filtering Methods for fMRI Datasets}, year = {1999}, month = {novembre}, journal = {NeuroImage}, volume = {10}, number = {5}, pages = {530-543}, url = {http://www.sciencedirect.com/science/article/pii/S1053811999904901}, keyword = {} }

2 - Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours. L. Blanc-Féraud et G. Aubert. International Journal of Computer Vision, 34(1): pages 19-28, septembre 1999. @ARTICLE{lbf99a, author = {Blanc-Féraud, L. and Aubert, G.}, title = {Some remarks on the equivalence between 2D and 3D classical snakes and geodesic active contours}, year = {1999}, month = {septembre}, journal = {International Journal of Computer Vision}, volume = {34}, number = {1}, pages = {19-28}, url = {http://link.springer.com/article/10.1023%2FA%3A1008168219878}, keyword = {} }

3 - 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.

4 - A Markov Pixon Information approach for low level image description. X. Descombes et F. Kruggel. IEEE Trans. Pattern Analysis ans Machine Intelligence, 21(6): pages 482-494, juin 1999. @ARTICLE{xd99b, author = {Descombes, X. and Kruggel, F.}, title = {A Markov Pixon Information approach for low level image description}, year = {1999}, month = {juin}, journal = {IEEE Trans. Pattern Analysis ans Machine Intelligence}, volume = {21}, number = {6}, pages = {482-494}, pdf = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=771311}, keyword = {} }

5 - Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline. T. Corbard et L. Blanc-Féraud et G. Berthomieu et J. Provost. Astronomy and Astrophysics, (344): pages 696-708, 1999. @ARTICLE{lbf99b, author = {Corbard, T. and Blanc-Féraud, L. and Berthomieu, G. and Provost, J.}, title = {Non linear regularization for helioseismic inversions. Application for the study of the solar tachocline}, year = {1999}, journal = {Astronomy and Astrophysics}, number = {344}, pages = {696-708}, url = {http://arxiv.org/abs/astro-ph/9901112}, keyword = {} }

6 - GMRF Parameter Estimation in a non-stationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging. X. Descombes et M. Sigelle et F. Prêteux. IEEE Trans. Image Processing, 8(4): pages 490-503, 1999. @ARTICLE{xd99a, author = {Descombes, X. and Sigelle, M. and Prêteux, F.}, title = {GMRF Parameter Estimation in a non-stationary Framework by a Renormalization Technique: Application to Remote Sensing Imaging}, year = {1999}, journal = {IEEE Trans. Image Processing}, volume = {8}, number = {4}, pages = {490-503}, url = {https://hal.archives-ouvertes.fr/hal-00272393}, keyword = {} }

7 - 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.

8 - Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm. A. Strandlie et J. Zerubia. Computer Physics Communications, 123(1-3): pages 77-87, 1999. @ARTICLE{jz99b, author = {Strandlie, A. and Zerubia, J.}, title = {Particle tracking with iterated Kalman filters and smoothers : the PMHT algorithm}, year = {1999}, journal = {Computer Physics Communications}, volume = {123}, number = {1-3}, pages = {77-87}, url = {http://www.sciencedirect.com/science/article/pii/S0010465599002581}, keyword = {} }

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Thèse de Doctorat et Habilitation

1 - Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales. A. Lorette. Thèse de Doctorat, Universite de Nice Sophia Antipolis, septembre 1999. Mots-clés : Texture, Segmentation, Champs de Markov, Morphologie mathematique, Zones urbaines. @PHDTHESIS{lorette99, author = {Lorette, A.}, title = {Analyse de Texture par Méthodes Markoviennes et par Morphologie Mathématique : Application à l'Analyse des Zones Urbaines sur des Images Satellitales}, year = {1999}, month = {septembre}, school = {Universite de Nice Sophia Antipolis}, pdf = {Theses/these-lorette.pdf}, keyword = {Texture, Segmentation, Champs de Markov, Morphologie mathematique, Zones urbaines} } Résumé : Dans cette thèse, nous nous intéressons au problème de l'analyse urbaine à partir d'images satellitales par des méthodes automatiques ou semi-automatiques issues du traitement d'image. Dans le premier chapitre, nous présentons le contexte dans lequel le travail a été effectué. Nous exposons les types de données utilisées, les approches statistiques considérées. Nous donnons également quelques exemples d'applications qui justifient une telle étude. Enfin, un état de l'art des diverses méthodes d'analyse des textures est présenté. Dans les deux chapitres suivants, nous développons une méthode automatique d'extraction d'un masque urbain à partir d'une analyse de la texture de l'image. Des méthodes d'extraction d'un masque urbain sont décrites. Ensuite, nous définissons plus précisemment les huit modèles markoviens gaussiens fondés sur des chaines. Ces modèles sont renormalisés par une méthode de renormalisation de groupe issue de la physique statistique afin de corriger le biais introduit par l'anisotropie du réseau de pixels. L'analyse de texture proposée est comparée avec deux méthodes classiques: les matrices de cooccurrence et les filtres de Gabor. L'image du paramètre de texture est ensuite classifiée avec un algorithme non supervisé de classification floue fondée sur la définition d'un critère entropique. Les paramètres estimés avec cet algorithme sont intégrés dans un modèle markovien de segmentation. Des résultats d'extraction de masques urbains sont finalement présentés sur des images satellitales optiques SPOT3, des simulations SPOT5, et des images radar ERS1. Dans le quatrième chapitre, nous présentons l'analyse granulométrique utilisée pour analyser le paysage urbain. Les outils et définitions de base de la morphologie mathématique sont exposés. Nous nous intéressons plus particulièrement à l'ouverture par reconstruction qui est utilisée comme transformation de base de la granulométrie. L'étape de quantification qui suit tout étape de transformation nous permet d'estimer en chaque pixel une distribution locale de taille qui est intégrée dans le terme d'attache aux données d'un modèle markovien de segmentation. Des tests sont effectués sur des simulations SPOT5. Abstract : In this thesis, we investigate the problem of urban areas analysis from satellite images by automatic or semi-automatic methods coming from image processing. In the first chapter, we describe the context of this work, i.e. the type of used data, the statistical applied methods. We also give some examples of the applications which require such an analysis. Finally, a study of the existing methods of texture analysis is presented. In the second and third chapter, we develop a non supervised method based on texture analysis in order to extract an urban mask. First a study of the existing methods of urban mask extraction is presented. Second we precisely describe the eight chain-based Gaussian Markovian models used to characterize urban texture. These models are normalized through a renormalization group technique derived from statistical physics in order to correct the bias introduced by the anisotropy of the lattice.The above mentionned method of texture analysis is then compared with two classical ones: coocurrences matrix and Gabor filters. The image is then partitionned by an unsupervised fuzzy Cmeans algorithm based on an entropic criterion. The final segmentation is performed by the minimization of an energy derived from a Markovian model. Some results are presented that are obtained from SPOT3 images, SPOT5 simulations and radar ERS1 images. In the fourth chapter, we present the granulometric approach used to segment within the urban area itself. The basic operations and definitions of mathematical morphology are settled. We are particularly interested in opening by reconstruction operation based on geodesic dilatations. In fact this operation is used to define a granulometry. The quantification step that follows the transformation step consists in estimating a local size distribution function for each pixel. These parameters are then integrated in the data term of a Markovian model. Some results on SPOT5 simulations are presented.

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14 Articles de conférence

1 - Two Markov point processes for simulating line networks. X. Descombes et R. Stoica et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Kobe, Japon, octobre 1999. @INPROCEEDINGS{xd99g, author = {Descombes, X. and Stoica, R. and Zerubia, J.}, title = {Two Markov point processes for simulating line networks}, year = {1999}, month = {octobre}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Kobe, Japon}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=822850}, keyword = {} }

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