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Publications of Caroline Lacoste
Result of the query in the list of publications :
3 Articles |
1 - Unsupervised line network extraction in remote sensing using a polyline process.
C. Lacoste and X. Descombes and J. Zerubia. Pattern Recognition, 43(4): pages 1631-1641, April 2010.
Keywords : Marked point process, Line networks, Road network extraction.
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{Unsupervised line network extraction in remote sensing using a polyline process}, |
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{2010}, |
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{http://dx.doi.org/10.1016/j.patcog.2009.11.003}, |
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{Marked point process, Line networks, Road network extraction} |
| } |
Abstract :
Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one. |
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2 - Point Processes for Unsupervised Line Network Extraction in Remote Sensing.
C. Lacoste and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, October 2005.
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3 - Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 170: pages 13--22, 2003.
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PhD Thesis and Habilitation |
1 - Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués.
C. Lacoste. PhD Thesis, Universite de Nice Sophia Antipolis, September 2004.
Keywords : Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process.
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{Stochastic geometry, Object extraction, RJMCMC, Line networks, Simulated Annealing, Marked point process} |
| } |
Résumé :
Cette thèse aborde le problème de l'extraction non supervisée des réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires et aériennes. Nous utilisons des processus objet, ou processus ponctuels marqués, comme modèles a priori. Ces modèles permettent de bénéficier de l'apport d'un cadre stochastique (robustesse au bruit, corpus algorithmique, etc.) tout en manipulant des contraintes géométriques fortes. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation.
Nous proposons tout d'abord une modélisation du réseau linéique par un processus dont les objets sont des segments interagissant entre eux. Le modèle a priori est construit de façon à exploiter au mieux la topologie du réseau recherché au travers de potentiels fondés sur la qualité de chaque interaction. Les propriétés radiométriques sont prises en compte dans un terme d'attache aux données fondé sur des mesures statistiques.
Nous étendons ensuite cette modélisation à des objets plus complexes. La manipulation de lignes brisées permet une extraction plus précise du réseau et améliore la détection des bifurcations.
Enfin, nous proposons une modélisation hiérarchique des réseaux hydrographiques dans laquelle les affluents d'un fleuve sont modélisés par un processus de lignes brisées dans le voisinage de ce fleuve.
Pour chacun des modèles, nous accélérons la convergence de l'algorithme MCMC par l'ajout de perturbations adaptées.
La pertinence de cette modélisation par processus objet est vérifiée sur des images satellitaires et aériennes, optiques et radar. |
Abstract :
This thesis addresses the problem of the unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. We use object processes, or marked point processes, as prior models. These models benefit from a stochastic framework (robustness w.r.t. noise, algorithms, etc.) while incorporating strong geometric constraints. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization.
We first propose to model line networks by a process whose objects are interacting line segments. The prior model is designed to exploit as fully as possible the topological properties of the network under consideration through potentials based on the quality of each interaction. The radiometric properties of the network are modeled using a data term based on statistical measures.
We then extend this model to more complex objects. The use of broken lines improves the detection of network junctions and increases the accuracy of the extracted network.
Finally, we propose a hierarchical model of hydrographic networks in which the tributaries of a given river are modeled by a process of broken lines in the neighborhood of this river. For each model, we accelerate convergence of the RJMCMC algorithm by using appropriate perturbations.
We show experimental results on aerial and satellite images (optical and radar data) to verify the relevance of the object process models. |
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6 Conference articles |
1 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, September 2005.
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2 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, September 2004.
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3 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, May 2004.
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{A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, |
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4 - 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.
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{Marked Point Process in Image Analysis : from Context to Geometry}, |
| year |
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{International Conference on Spatial Point Process Modelling and its Application (SPPA)}, |
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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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5 - Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. In Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, September 2003.
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6 - Road Network Extraction in Remote Sensing by a Markov Object Process.
C. Lacoste and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, September 2003.
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3 Technical and Research Reports |
1 - Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework.
C. Lacoste and X. Descombes and J. Zerubia and N. Baghdadi. Research Report 5697, INRIA, France, September 2005.
@TECHREPORT{rrHimne,
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{Hydrographic Network Extraction from Radar Satellite Imagesusing a Hierarchical Model within a Stochastic Geometry Framework}, |
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Résumé :
Ce rapport présente un algorithme d'extraction non supervisée de réseaux hydrographiques à partir d'images satellitaires exploitant la structure arborescante de tels réseaux. L'extraction du surfacique (branches de largeur supérieure à trois pixels) est réalisée par un algorithme efficace fondé sur une modélisation par champ de Markov. Ensuite, l'extraction du linéique se fait par un algorithme récursif fondé sur un modèle hiérarchique dans lequel les affluents d'un fleuve donné sont modélisés par un processus ponctuel marqué défini dans le voisinage de ce fleuve. L'optimisation de chaque processus ponctuel est réalisée par un recuit simulé utilisant un algorithme de Monte Carlo par chaîne de Markov à sauts réversibles. Nous obtenons de bons résultats en terme d'omissions et de surdétections sur une image radar de type ERS. |
Abstract :
This report presents a two-step algorithm for unsupervised extraction of hydrographic networks from satellite images, that exploits the tree structures of such networks. First, the thick branches of the network are detected by an efficient algorithm based on a Markov random field. Second, the line branches are extracted using a recursive algorithm based on a hierarchical model of the hydrographic network, in which the tributaries of a given river are modeled by an object process (or a marked point process) defined within the neighborhood of this river. Optimization of each point process is done via simulated annealing using a reversible jump Markov chain Monte Carlo algorithm. We obtain encouraging results in terms of omissions and overdetections on a radar satellite image. |
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2 - A Polyline Process for Unsupervised Line Network Extraction in Remote Sensing.
C. Lacoste and X. Descombes and J. Zerubia. Research Report 5698, INRIA, France, September 2005.
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Résumé :
Ce rapport présente un nouveau modèle issu de la géométrie stochastique pour l'extraction non supervisée de réseaux linéiques (routes, rivières, etc.) à partir d'images satellitaires ou aériennes. Le réseau linéique présent dans la scène observée est modélisé par un processus de lignes brisées, appelé CAROLINE. Le modèle a priori incorpore de fortes contraintes géométriques et topologiques au travers de potentiels sur la forme des lignes brisées et de potentiels d'interaction. Les propriétés radiométriques sont incorporées via la construction d'un terme d'attache aux données fondé sur des tests statistiques. Un recuit simulé sur un algorithme de type Monte Carlo par Chaîne de Markov (MCMC) à sauts réversibles permet une optimisation globale sur l'espace des configurations d'objets, indépendamment de l'initialisation. L'ajout de perturbations pertinentes permet une accélération de la convergence de l'algorithme. Des résultats expérimentaux obtenus sur des images satellitaires et aériennes sont présentés et comparés à ceux obtenus avec un précédent modèle fondé sur un processus de segments, appelé Quality Candy. |
Abstract :
This report presents a new stochastic geometry model for unsupervised extraction of line networks (roads, rivers, etc.) from remotely sensed images. The line network in the observed scene is modeled by a polyline process, named CAROLINE. The prior model incorporates strong geometrical and topological constraints through potentials on the polyline shape and interaction potentials. Data properties are taken into account through a data term based on statistical tests. Optimization is done via a simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm, without any specific initialization. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Experimental results are provided on aerial and satellite images and compared with the results obtained with a previous model, that is a segment process called Quality Candy. |
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3 - A Comparative Study of Point Processes for Line Network Extraction in Remote Sensing.
C. Lacoste and X. Descombes and J. Zerubia. Research Report 4516, Inria, France, July 2002.
Keywords : Stochastic geometry, Marked point process, Road network, Line networks, RJMCMC.
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Résumé :
Nous présentons, dans ce rapport, une étude comparative entre plusieurs modèles d'extraction de réseaux linéiques, issus de la géométrie stochastique. Nous nous pla ons dans le cadre des processus ponctuels marqués spécifiés par une densité par rapport au processus de Poisson homogène. L'objectif de cette étude est de déterminer quelle type de densité a priori est la plus adaptée à cette probématique de détection de réseaux linéiques, et plus particulièrement de réseaux routiers. Nous reprenons le Candy modèle, introduit dans [21] pour l'extraction de réseaux routiers, et nous l'utilisons comme modèle de référence. Ce modèle est basé sur l'idée qu'un réseau routier peut être assimilé à une réalisation d'un processus Markov objet, où les objets correspondent à des segments en interaction. Nous proposons deux variantes de ce modèle qui font intervenir des coefficients mesurant la qualité des interactions entre objets. La première est une généralisation du Candy modèle et la seconde correspond à une adaptation du modèle IDQ, proposé dans [13] pour l'extraction de bâtiments dans les modèles numériques d'élévation. Nous réalisons l'optimisation de chaque modèle par un recuit simulé sur un algorithme MCMC à sauts réversibles. Les résultats expérimentaux obtenus pour les trois modèles, sur des images satellitaires ou aériennes, permettent de vérifier l'intérêt de l'intégration de la qualité des interactions dans la densité a priori. |
Abstract :
We present in this report a comparative study between models of line network extraction, within a stochastic geometry framework. We rely on the theory of marked point processes specified by a density with respect to the uniform Poisson process. We aim to determine which prior density is the most relevant for road network detection. The Candy model, introduced in [21] for the extraction of road networks, is used as a reference model. This model is based on the idea that a road network can be thought of as a realization of a Markov object process, where the objects correspond to interacting line segments. We have developed two variants of this model which use quality coefficients for interactions. The first of these two variants is a generalization of the Candy model and the second one is an adaptation of the IDQ model proposed in [13] for the problem of building extraction from digital elevation models. The optimization is achieved by a simulated annealing with a RJMCMC algorithm. The experimental results, obtained for each model on aerial or satellite images, show the interest of adding quality coefficients for interactions in the prior density. |
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