Publications de 2011

Résultat de la recherche dans la liste des publications :

• 7 Articles
• 1 Thèse de Doctorat et Habilitation
• 19 Articles de conférence
• 4 Rapports de recherche et Rapports techniques
• 2 Livres

nouvelle recherche | bibtex

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7 Articles

1 - Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE Journal of Selected Topics in Signal Processing, 5(3): pages 554-566, juin 2011. Mots-clés : Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula. Copyright : IEEE @ARTICLE{krylovJSTSP2011, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Supervised High Resolution Dual Polarization SAR Image Classification by Finite Mixtures and Copulas}, year = {2011}, month = {juin}, journal = { IEEE Journal of Selected Topics in Signal Processing}, volume = {5}, number = {3}, pages = {554-566}, url = {http://dx.doi.org/10.1109/JSTSP.2010.2103925}, pdf = {http://hal.archives-ouvertes.fr/inria-00562326/en/}, keyword = {Polarimetric synthetic aperture radar, Supervised classification, probability density function (pdf), dictionary-based pdf estimation, Markov random field, copula} } Abstract : In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images. A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate distribution modeling. The finite mixture modeling is performed via a recently proposed SAR-specific dictionarybased stochastic expectation maximization approach to SAR amplitude pdf estimation. For modeling the joint distribution of dual-pol data the statistical concept of copulas is employed, and a novel copula-selection dictionary-based method is proposed. In order to take into account the contextual information, the developed joint pdf model is combined with a Markov random field approach for Bayesian image classification. The accuracy of the developed dual-pol supervised classification approach is validated and compared with benchmark approaches on two high resolution dual-pol TerraSAR-X scenes, acquired during an epidemiological study. A corresponding single-channel version of the classification algorithm is also developed and validated on a single polarization COSMO-SkyMed scene.

2 - An automatic counter for aerial images of aggregations of large birds. S. Descamps et A. Béchet et X. Descombes et A. Arnaud et J. Zerubia. Bird Study, : pages 1-7, juin 2011. @ARTICLE{BirdStudy, author = {Descamps, S. and Béchet, A. and Descombes, X. and Arnaud, A. and Zerubia, J.}, title = {An automatic counter for aerial images of aggregations of large birds}, year = {2011}, month = {juin}, journal = {Bird Study}, pages = {1-7}, url = {http://hal.inria.fr/inria-00624587}, pdf = {http://www-sop.inria.fr/ariana/Publis/Descamps2011BS.pdf}, keyword = {} }

3 - On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection. P. Weiss et A. Fournier et L. Blanc-Féraud et G. Aubert. SIAM Journal on Imaging Sciences, 4(1): pages 448-471, mars 2011. Mots-clés : Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing. @ARTICLE{SIIMS_2011, author = {Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, title = {On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection}, year = {2011}, month = {mars}, journal = {SIAM Journal on Imaging Sciences}, volume = {4}, number = {1}, pages = {448-471}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, keyword = {Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing} } Abstract : We analyze the illumination invariance of the level lines of an image. We show that if the scene surface has Lambertian reflectance and the light is directed, then a necessary and sufficient condition for the level lines to be illumination invariant is that the three-dimensional scene be developable and that its albedo satisfy some geometrical constraints. We then show that the level lines are “almost” invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. This allows us to devise a fast and simple algorithm that detects changes between pairs of remotely sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of the algorithm both on synthetic OpenGL scenes and real QuickBird images. The synthetic results illustrate the theory developed in this paper. The two real QuickBird images show that the proposed change detection algorithm is discriminant. For easy scenes it achieves a rate of 85% detected changes for 10% false positives, while it reaches a rate of 75% detected changes for 25% false positives on demanding scenes.

4 - Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data. V. Krylov et G. Moser et S.B. Serpico et J. Zerubia. IEEE-Geoscience and Remote Sensing Letters, 8(1): pages 148-152, janvier 2011. Mots-clés : finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (SEM), synthetic aperture radar. Copyright : IEEE @ARTICLE{krylovGRSL2011, author = {Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data}, year = {2011}, month = {janvier}, journal = {IEEE-Geoscience and Remote Sensing Letters}, volume = {8}, number = {1}, pages = {148-152}, url = {http://dx.doi.org/10.1109/LGRS.2010.2053517}, pdf = {http://hal.archives-ouvertes.fr/inria-00503893/en/}, keyword = {finite mixture models, parametric estimation, probability-density-function estimation, EM Stochastique (SEM), synthetic aperture radar} } Abstract : In this letter, we address the problem of estimating the amplitude probability density function (pdf) of single-channel synthetic aperture radar (SAR) images. A novel flexible method is developed to solve this problem, extending the recently proposed dictionary-based stochastic expectation maximization approach (developed for a medium-resolution SAR) to very high resolution (VHR) satellite imagery, and enhanced by introduction of a novel procedure for estimating the number of mixture components, that permits to reduce appreciably its computational complexity. The specific interest is the estimation of heterogeneous statistics, and the developed method is validated in the case of the VHR SAR imagery, acquired by the last-generation satellite SAR systems, TerraSAR-X and COSMO-SkyMed. This VHR imagery allows the appreciation of various ground materials resulting in highly mixed distributions, thus posing a difficult estimation problem that has not been addressed so far. We also conduct an experimental study of the extended dictionary of state-of-the-art SAR-specific pdf models and consider the dictionary refinements.

5 - Multiple Birth and Cut Algorithm for Multiple Object Detection. A. Gamal Eldin et X. Descombes et Charpiat G. et J. Zerubia. Journal of Multimedia Processing and Technologies, 2011. Mots-clés : Markov point process, Multiple Birth and Cut, Graph Cut, Belief Propagation, flamingo counting. @ARTICLE{MBC_BP10, author = {Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, title = {Multiple Birth and Cut Algorithm for Multiple Object Detection}, year = {2011}, journal = {Journal of Multimedia Processing and Technologies}, url = {http://hal.inria.fr/hal-00616371}, keyword = {Markov point process, Multiple Birth and Cut, Graph Cut, Belief Propagation, flamingo counting} } Abstract : In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing their respective advantages and drawbacks, where the most important advantage of the MBC is the reduction of number of parameters. We demonstrate that by proposing good candidates throughout the selection phase in the birth step, the speed of convergence is increased. In this selection phase, the best candidates are chosen from object sets by a belief propagation algorithm. We validate our algorithm on the flamingo counting problem in a colony and demonstrate that our algorithm outperforms the MBD algorithm.

6 - A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images. M. S. Kulikova et I. H. Jermyn et X. Descombes et E. Zhizhina et J. Zerubia. International Journal of Computer Vision and Image Processing, 1(2): pages 1-12, 2011. Mots-clés : Contour actif, Processus ponctuels marques, multiple birth-and-death dynamics, multiple object extraction, Shape prior. @ARTICLE{kulikova_ijcvip2010, author = {Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, title = {A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images}, year = {2011}, journal = {International Journal of Computer Vision and Image Processing}, volume = {1}, number = {2}, pages = {1-12}, url = {http://hal.archives-ouvertes.fr/hal-00804118}, keyword = {Contour actif, Processus ponctuels marques, multiple birth-and-death dynamics, multiple object extraction, Shape prior} } Abstract : Object extraction from images is one of the most important tasks in remote sensing image analysis. For accurate extraction from very high resolution (VHR) images, object geometry needs to be taken into account. A method for incorporating strong yet flexible prior shape information into a marked point process model for the extraction of multiple objects of complex shape is presented. To control the computational complexity, the objects considered are defined using the image data and the prior shape information. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process on the space of multiple objects. The authors present several experimental results on the extraction of tree crowns from VHR aerial images.

7 - Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires. S. Ben Hadj et F. Chatelain et X. Descombes et J. Zerubia. Revue Française de Photogrammétrie et de Télédétection (SFPT), (194): pages 2-15, 2011. Mots-clés : processus ponctuel marqué, RJMCMC, Recuit Simule, SEM, pseudo-vraisemblance, extraction d'objet.. @ARTICLE{RFPT_SBH_11, author = {Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Approche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires}, year = {2011}, journal = {Revue Française de Photogrammétrie et de Télédétection (SFPT)}, number = {194}, pages = {2-15}, url = {http://hal.inria.fr/hal-00638665}, keyword = {processus ponctuel marqué, RJMCMC, Recuit Simule, SEM, pseudo-vraisemblance, extraction d'objet.} }

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Thèse de Doctorat et Habilitation

1 - Processus ponctuels et algorithmes de coupure minimal de graphe appliqués à l'extraction d'objets 2D et 3D. A. Gamal Eldin. Thèse de Doctorat, Universite de Nice Sophia Antipolis, octobre 2011. Mots-clés : Multiple object detection, Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process, Stochastic geometry. @PHDTHESIS{GamalPhdThesis, author = {Gamal Eldin, A.}, title = {Processus ponctuels et algorithmes de coupure minimal de graphe appliqués à l'extraction d'objets 2D et 3D}, year = {2011}, month = {octobre}, school = {Universite de Nice Sophia Antipolis}, url = {http://tel.archives-ouvertes.fr/tel-00737988}, keyword = {Multiple object detection, Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process, Stochastic geometry} } Résumé : L'objectif de cette thèse est de développer une nouvelle approche de détection d'objets 3D à partir d'une image 2D, prenant en compte les occultations et les phénomènes de perspective. Cette approche est fondée sur la théorie des processus ponctuels marqués, qui a fait ses preuves dans la solution de plusieurs problèmes en imagerie haute résolution. Le travail de la thèse est structuré en deux parties: En première partie: Nous proposons une nouvelle méthode probabiliste pour gérer les occultations et les effets de perspective. Le modèle propose est fondé sur la simulation d'une scène 3D utilisant OpenGL sur une carte graphique (GPU). C'est une méthode orientée objet, intégrée dans le cadre d'un processus ponctuel marqué. Nous l'appliquons pour l'estimation de la taille d'une colonie de manchots, là où nous modélisons une colonie de manchots comme un nombre inconnu d'objets 3D. L'idée principale de l'approche proposée consiste à échantillonner certaines configurations candidat composé d'objets 3D s'appuyant sur le plan réel. Une densité de Gibbs est définie sur l'espace des configurations, qui prend en compte des informations a priori et sur les données. Pour une configuration proposée, la scène est projetée sur le plan image, et les configurations sont modifiées jusqu'à convergence. Pour évaluer une configuration proposée, nous mesurons la similarité entre l'image projetée de la configuration proposée et l'image réelle, définissant ainsi le terme d'attache aux données et l'a priori pénalisant les recouvrements entre objets. Nous avons introduit des modifications dans l'algorithme d'optimisation pour prendre en compte les nouvelles dépendances qui existent dans notre modèle 3D. En deuxième partie: Nous proposons une nouvelle méthode d'optimisation appelée Abstract : The topic of this thesis is to develop a novel approach for 3D object detection from a 2D image. This approach takes into consideration the occlusions and the perspective effects. This work has been embedded in a marked point process framework, proved to be efficient for solving many challenging problems dealing with high resolution images. The accomplished work during the thesis can be presented in two parts: First part: We propose a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is based on 3D scene simulation on the GPU using OpenGL. It is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying on the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. The proposed configurations are projected onto the image plane, and the configurations are modified until convergence. To evaluate a proposed configuration, we measure the similarity between the projected image of the proposed configuration and the real image, by defining a data term and a prior term which penalize objects overlapping. We introduced modifications to the optimization algorithm to take into account new dependencies that exists in our 3D model. Second part: We propose a new optimization method which we call

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19 Articles de conférence

1 - Building large urban environments from unstructured point data. F. Lafarge et C. Mallet. Dans Proc. IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, novembre 2011. @INPROCEEDINGS{lafarge_iccv11, author = {Lafarge, F. and Mallet, C.}, title = {Building large urban environments from unstructured point data}, year = {2011}, month = {novembre}, booktitle = {Proc. IEEE International Conference on Computer Vision (ICCV)}, address = {Barcelona, Spain}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126353}, keyword = {} }

2 - Synthetic Aperture Radar Image Classification via Mixture Approaches. V. Krylov et J. Zerubia. Dans Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, novembre 2011. Mots-clés : Radar a Ouverture Synthetique (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution. Copyright : IEEE @INPROCEEDINGS{krylovCOMCAS11, author = {Krylov, V. and Zerubia, J.}, title = {Synthetic Aperture Radar Image Classification via Mixture Approaches}, year = {2011}, month = {novembre}, booktitle = {Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, address = {Tel Aviv, Israel}, url = {http://www.ortra.biz/comcas/}, pdf = {http://hal.archives-ouvertes.fr/inria-00625551/en/}, keyword = {Radar a Ouverture Synthetique (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} } Abstract : In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors.

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