Publications about RJMCMC

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• 2 Articles
• 4 PhD Thesis and Habilitations
• 8 Conference articles
• 12 Technical and Research Reports
• 1 Collection article or Book chapter

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8 Conference articles

5 - Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application. G. Perrin and X. Descombes and J. Zerubia. In Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), St Augustine, Florida, USA, November 2005. Keywords : Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC. Copyright : Springer Verlag @INPROCEEDINGS{perrin_emmcvpr05, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Adaptive Simulated Annealing for Energy Minimization Problem in a Marked Point Process Application}, year = {2005}, month = {November}, booktitle = {Proc. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)}, address = {St Augustine, Florida, USA}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_emmcvpr.ps.gz}, keyword = {Simulated Annealing, Marked point process, Stochastic geometry, MAP estimation, RJMCMC} } Abstract : We use marked point processes to detect an unknown number of trees from high resolution aerial images. This is in fact an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects to the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects. We compare here different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels.

6 - A Marked Point Process Model for Tree Crown Extraction in Plantations. G. Perrin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Genoa, Italy, September 2005. Keywords : Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process. @INPROCEEDINGS{perrin_icip05, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {A Marked Point Process Model for Tree Crown Extraction in Plantations}, year = {2005}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Genoa, Italy}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_icip05.ps.gz}, keyword = {Stochastic geometry, RJMCMC, Tree Crown Extraction, Object extraction, Marked point process} } Abstract : This work presents a framework to extract tree crowns from remotely sensed data, especially in plantation images, using stochastic geometry. We aim at finding the tree top positions, and the tree crown diameter distribution. Our approach consists in considering that these images are some realizations of a marked point process. First we model the tree plantation as a configuration of an unknown number of ellipses. Then, a Bayesian energy is defined, containing both a prior energy which incorporates the prior knowledge of the plantation geometric properties, and a likelihood which fits the objects to the data. Eventually, we estimate the global minimum of this energy using Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing scheme. We present results on optical aerial images of poplars provided by IFN.

7 - Tree Crown Extraction using Marked Point Processes. G. Perrin and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004. Keywords : RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry. @INPROCEEDINGS{perrin04a, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Tree Crown Extraction using Marked Point Processes}, year = {2004}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {University of Technology, Vienna, Austria}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/perrin_eusipco2004.ps.gz}, keyword = {RJMCMC, Marked point process, Simulated Annealing, Tree Crown Extraction, Object extraction, Stochastic geometry} } Abstract : In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process. Then, we use a Reversible Jump Markov Chain Monte Carlo dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels. Results are shown on aerial images of poplars provided by IFN.

8 - Marked Point Process in Image Analysis : from Context to Geometry. X. Descombes and F. Kruggel and C. Lacoste and M. Ortner and G. Perrin and J. Zerubia. In International Conference on Spatial Point Process Modelling and its Application (SPPA), Castellon, Spain, 2004. Keywords : RJMCMC, Object extraction, Marked point process, Stochastic geometry. @INPROCEEDINGS{geostoch04a, author = {Descombes, X. and Kruggel, F. and Lacoste, C. and Ortner, M. and Perrin, G. and Zerubia, J.}, title = {Marked Point Process in Image Analysis : from Context to Geometry}, year = {2004}, booktitle = {International Conference on Spatial Point Process Modelling and its Application (SPPA)}, address = {Castellon, Spain}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.pdf}, ps = {ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.ps.gz}, keyword = {RJMCMC, Object extraction, Marked point process, Stochastic geometry} } Abstract : We consider the marked point process framework as a natural extension of the Markov random field approach in image analysis. We consider a general model defined by its density allowing us to consider some geometrical constraints on objects and between objects in feature extraction problems. Some examples are derived for small brain lesions detection from MR Images, road network, tree crown and building extraction from remotely sensed images. The results obtained on real data show the relevance of the proposal approach.

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12 Technical and Research Reports

1 - Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution . S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. Rapport de recherche 7350, INRIA, July 2010. Keywords : Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction. @TECHREPORT{RR-7350, author = {Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Estimation des paramètres de modèles de processus ponctuels marqués pour l'extraction d'objets en imagerie spatiale et aérienne haute résolution }, year = {2010}, month = {July}, institution = {INRIA}, type = {Rapport de recherche}, number = {7350}, url = {http://hal.archives-ouvertes.fr/inria-00508431/fr/}, keyword = {Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM), pseudo-vraisemblance, Object extraction} }

2 - A structural approach for 3D building reconstruction. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 6048, INRIA, November 2006. Keywords : 3D reconstruction, Structural approach, Building, RJMCMC, Viterbi. @TECHREPORT{Lafarge_rr_6048, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {A structural approach for 3D building reconstruction}, year = {2006}, month = {November}, institution = {INRIA}, type = {Research Report}, number = {6048}, url = {https://hal.inria.fr/inria-00114338}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2006_Lafarge_rr_6048.pdf}, keyword = {3D reconstruction, Structural approach, Building, RJMCMC, Viterbi} }

3 - A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models.. M. Ortner and X. Descombes and J. Zerubia. Research Report 5712, INRIA, France, October 2005. Keywords : Marked point process, Buildings, RJMCMC. @TECHREPORT{ortner-RR05, author = {Ortner, M. and Descombes, X. and Zerubia, J.}, title = {A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models.}, year = {2005}, month = {October}, institution = {INRIA}, type = {Research Report}, number = {5712}, address = {France}, url = {https://hal.inria.fr/inria-00070305}, keyword = {Marked point process, Buildings, RJMCMC} } Résumé : Ce travail présente une approche par géométrie stochastique pour l'extraction de primitives dans les images. Ces structures sont modélisées sous forme de réalisations d'un processus ponctuel spatial marqué dont les points sont des formes géométriques. Cette approche permet d'incorporer un modèle a priori sur la répartition spatiale des structures d'intérêt. Plus spécifiquement, nous présentons un modèle fondé sur l'interaction d'un processus de rectangles avec un processus de segments. Le premier est dédié à la détection des zones homogènes dans l'image et le second à la détection des discontinuités significatives. Nous définissons l'énergie d'une configuration de façon à favoriser la connection entre les segments, l'alignement des rectangles et l'adéquation entre les deux types de primitives. L'estimation repose sur l'emploi d'une technique de recuit-simulé. Le modèle proposé est appliqué à l'analyse de Modèles Numériques d'Elevation. Nous présentons des résultats sur des données réelles fournies par l'Institut Géographique National (IGN). Nous montrons en particulier que l'approche est efficace sur des données de types très différents. Abstract : A marked point process of rectangles and segments for automatic analysis of Digital Elevation Models. This work presents a framework for automatic feature extraction from images using stochastic geometry. Features in images are modeled as realizations of a spatial point process of geometrical shapes. This framework allows the incorporation of a prior knowledge on the spatial repartition of features. More specifically, we present a model based on the superposition of a process of segments and a process of rectangles. The former is dedicated to the detection of linear networks of discontinuities, while the latter aims at segmenting homogeneous areas. An energy is defined, favoring connections of segments, alignments of rectangles, as well as a relevant interaction between both types of objects. The estimation is performed by minimizing the energy using a simulated annealing algorithm. The proposed model is applied to the analysis of Digital Elevation Models (DEMs). These images are raster data representing the altimetry of a dense urban area. We present results on real data provided by the IGN (French National Geographic Institute) consisting in low quality DEMs of various types.

4 - A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. Research Report 5687, INRIA, France, September 2005. Keywords : 3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM). @TECHREPORT{5687, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {A Parametric Model for Automatic 3D Building Reconstruction from High Resolution Satellite Images}, year = {2005}, month = {September}, institution = {INRIA}, type = {Research Report}, number = {5687}, address = {France}, url = {http://hal.inria.fr/inria-00070326/fr/}, pdf = {https://hal.inria.fr/file/index/docid/70326/filename/RR-5687.pdf}, ps = {http://hal.inria.fr/docs/00/07/03/26/PS/RR-5687.ps}, keyword = {3D reconstruction, Buildings, RJMCMC, Digital Elevation Model (DEM)} } Résumé : Dans ce rapport, nous développons un modèle paramétrique pour la reconstruction automatique de bâtiments en 3D fondé sur une approche bayésienne à partir de simulations PLEIADES. Les images satellitaires haute résolution représentent un nouveau type de données permettant de traiter les problèmes de reconstruction 3D de bâtiments. Leur résolution ``relativement basse'' et leur faible rapport signal sur bruit pour ce type de problèmes ne permet pas l'utilisation des méthodes standard développées dans le cas des images aériennes. Nous proposons une approche paramétrique utilisant des Modèles Numériques d'Elévation (MNE) et les empreintes de bâtiments associées modélisées par rectangles. La méthode proposée est fondée sur une approche bayésienne. Une technique de type de Monte Carlo par Chaînes de Markov est utilisée afin d'optimiser le modèle énergétique. Abstract : This report develops a parametric model for automatic 3D building reconstruction based on a Bayesian approach from PLEIADES simulations. High resolution satellite images are a new kind of data to deal with 3D building reconstruction problems. Their ``relatively low'' resolution and low signal noise ration do not allow to use standard methods developed for the aerial image case. We propose a parametric approach using Digital Elevation Models (DEM) and associated rectangular building footprints. The proposed method is based on a Bayesian approach. A Markov Chain Monte Carlo technique is used to optimize the energy model.

5 - Point Processes in Forestry : an Application to Tree Crown Detection. G. Perrin and X. Descombes and J. Zerubia. Research Report 5544, INRIA, France, April 2005. Keywords : Marked point process, Object extraction, RJMCMC, Tree Crown Extraction, Stochastic geometry. @TECHREPORT{5544, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Point Processes in Forestry : an Application to Tree Crown Detection}, year = {2005}, month = {April}, institution = {INRIA}, type = {Research Report}, number = {5544}, address = {France}, url = {https://hal.inria.fr/inria-00070463}, pdf = {https://hal.inria.fr/file/index/docid/70463/filename/RR-5544.pdf}, ps = {https://hal.inria.fr/docs/00/07/04/63/PS/RR-5544.ps}, keyword = {Marked point process, Object extraction, RJMCMC, Tree Crown Extraction, Stochastic geometry} } Résumé : Dans ce rapport de recherche, notre but est d'extraire des houppiers à partir d'images aériennes de forêts à l'aide de processus ponctuels marqués de disques et d'ellipses. Notre approche consiste, en effet, à modéliser les données comme des réalisations de tels processus. Une fois l'objet géométrique de référence choisi, nous échantillonnons le processus objet défini par une densité grâce à un algorithme MCMC à sauts réversibles, optimisé par un recuit simulé afin d'extraire le maximum a posteriori de cette densité. Cette configuration optimale nous donnera l'extraction recherchée. Dans une première partie, nous proposons de revenir quelque peu sur les processus ponctuels marqués et leur application dans la foresterie. Puis, nous présentons deux nouveaux modèles d'extraction de houppiers à base de disques et d'ellipses, et discutons de quelques améliorations au niveau de la simulation et de l'optimisation de notre algorithme. Nous présentons des résultats obtenus sur des images aériennes très haute résolution fournies par l'Inventaire Forestier National (IFN), ainsi que sur des images synthétiques simulées avec le logiciel AMAP (Bionatics, projet Digiplante). Abstract : In this research report, we aim at extracting tree crowns from remotely sensed images using marked point processes of discs and ellipses. Our approach is indeed to consider that the data are some realizations of a marked point process. Once a geometrical object is defined, we sample a marked point process defined by a density with a Reversible Jump Markov Chain Monte Carlo dynamics and simulated annealing to get the maximum a posteriori estimator of the tree crown distribution on the image. In a first part, we propose to review the basis of marked point processes and some of their examples used in forestry statistic inference. Then, we present two new models, with discs and ellipses, and discuss some improvements made in the optimization or in the simulation. Results are shown on high resolution aerial images of poplars provided by the French Forest Inventory (IFN), and synthetic images simulated with AMAP software (Bionatics, Digiplante project).

6 - Extraction de Houppiers par Processus Objet. G. Perrin and X. Descombes and J. Zerubia. Research Report 5037, INRIA, France, December 2003. Keywords : Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process, RJMCMC. @TECHREPORT{Perrin03, author = {Perrin, G. and Descombes, X. and Zerubia, J.}, title = {Extraction de Houppiers par Processus Objet}, year = {2003}, month = {December}, institution = {INRIA}, type = {Research Report}, number = {5037}, address = {France}, url = {https://hal.inria.fr/inria-00071547}, pdf = {https://hal.inria.fr/file/index/docid/71547/filename/RR-5037.pdf}, ps = {https://hal.inria.fr/docs/00/07/15/47/PS/RR-5037.ps}, keyword = {Object extraction, Tree Crown Extraction, Stochastic geometry, Marked point process, RJMCMC} } Résumé : Nous cherchons à extraire des houppiers à partir d'images de télédétection. Pour ce faire, nous construisons un processus objet et assimilons nos images d'arbres à des réalisations de ce processus. La première étape consiste à définir d'une part les objets géométriques modélisant les arbres, et d'autre part la densité du processus à simuler.La seconde étape consiste à construire un algorithme MCMC à sauts réversibles, et une estimée de la configuration d'objets. Les transitions aléatoires de la chaîne sont régies par des noyaux de propositions, chacun étant associé à une perturbation.Nous testons notre modèle sur des images aériennes de peupleraies fournies par l'IFN. Abstract : In this paper we aim at extracting tree crowns from remotely sensed images. Our approach is to consider that these images are some realizations of a marked point process. The first step is to define the geometrical objects that design the trees, and the density of the process.Then, we use a reversible jump MCMC dynamics and a simulated annealing to get the maximum a posteriori estimator of the tree crowns distribution on the image. Transitions of the Markov chain are managed by some specific proposition kernels.Results are shown on aerial images of poplars given by IFN.

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