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Publications de Caroline Lacoste
Résultat de la recherche dans la liste des publications :
3 Articles |
1 - Unsupervised line network extraction in remote sensing using a polyline process.
C. Lacoste et X. Descombes et J. Zerubia. Pattern Recognition, 43(4): pages 1631-1641, avril 2010.
Mots-clés : Processus ponctuels marques, Reseaux lineiques, Road network extraction.
@ARTICLE{lacoste10,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Unsupervised line network extraction in remote sensing using a polyline process}, |
| year |
= |
{2010}, |
| month |
= |
{avril}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{43}, |
| number |
= |
{4}, |
| pages |
= |
{1631-1641}, |
| url |
= |
{http://dx.doi.org/10.1016/j.patcog.2009.11.003}, |
| keyword |
= |
{Processus ponctuels marques, Reseaux lineiques, 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 et X. Descombes et J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(10): pages 1568-1579, octobre 2005.
@ARTICLE{lacoste05,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
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{Point Processes for Unsupervised Line Network Extraction in Remote Sensing}, |
| year |
= |
{2005}, |
| month |
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{octobre}, |
| journal |
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{IEEE Trans. Pattern Analysis and Machine Intelligence}, |
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{27}, |
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{10}, |
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{1568-1579}, |
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{} |
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|
3 - Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Bulletin de la Société Française de Photogrammétrie et de Télédétection, 170: pages 13--22, 2003.
@ARTICLE{lacostesfpt,
|
| author |
= |
{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
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{Extraction automatique des réseaux linéiques à partir d'images satellitaires et aériennes par processus Markov objet}, |
| year |
= |
{2003}, |
| journal |
= |
{Bulletin de la Société Française de Photogrammétrie et de Télédétection}, |
| volume |
= |
{170}, |
| pages |
= |
{13--22}, |
| url |
= |
{http://www.researchgate.net/profile/Nicolas_Baghdadi/publication/236882132_Extraction_automatique_des_rseaux_liniques__partir_dimages_satellitaires_et_ariennes_par_processus_Markov_objets/links/00463519e05ebd9e83000000.pdf?disableCoverPage=true}, |
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|
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Thèse de Doctorat et Habilitation |
1 - Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués.
C. Lacoste. Thèse de Doctorat, Universite de Nice Sophia Antipolis, septembre 2004.
Mots-clés : Geometrie stochastique, Extraction d'objets, RJMCMC, Reseaux lineiques, Recuit Simule, Processus ponctuels marques.
@PHDTHESIS{lacoste_these,
|
| author |
= |
{Lacoste, C.}, |
| title |
= |
{Extraction de Réseaux Linéiques à partir d'Images Satellitaires et Aériennes par Processus Ponctuels Marqués}, |
| year |
= |
{2004}, |
| month |
= |
{septembre}, |
| school |
= |
{Universite de Nice Sophia Antipolis}, |
| url |
= |
{https://hal.inria.fr/tel-00261397}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/26/13/97/PDF/THESE_CAROLINE_LACOSTE.pdf}, |
| keyword |
= |
{Geometrie stochastique, Extraction d'objets, RJMCMC, Reseaux lineiques, Recuit Simule, Processus ponctuels marques} |
| } |
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 Articles de conférence |
1 - Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. European Signal Processing Conference (EUSIPCO), Antalya, Turkey, septembre 2005.
@INPROCEEDINGS{lacoste_eusipco05,
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{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
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{Extraction of hydrographic networks from satellite images using a hierarchical model within a stochastic geometry framework}, |
| year |
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{2005}, |
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{septembre}, |
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{Proc. European Signal Processing Conference (EUSIPCO)}, |
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{Antalya, Turkey}, |
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{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078007}, |
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2 - Unsupervised line network extraction from remotely sensed images by polyline process.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. European Signal Processing Conference (EUSIPCO), University of Technology, Vienna, Austria, septembre 2004.
@INPROCEEDINGS{lacoste04b,
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| author |
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{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
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{Unsupervised line network extraction from remotely sensed images by polyline process}, |
| year |
= |
{2004}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
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{University of Technology, Vienna, Austria}, |
| url |
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{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7079995}, |
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{http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1608.pdf}, |
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{} |
| } |
|
3 - A Bayesian Geometric Model for Line Network Extraction from Satellite Images.
C. Lacoste et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Montreal, Quebec, Canada, mai 2004.
@INPROCEEDINGS{lacoste04a,
|
| author |
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{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
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{A Bayesian Geometric Model for Line Network Extraction from Satellite Images}, |
| year |
= |
{2004}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| address |
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{Montreal, Quebec, Canada}, |
| url |
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{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1326607}, |
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|
4 - Marked Point Process in Image Analysis : from Context to Geometry.
X. Descombes et F. Kruggel et C. Lacoste et M. Ortner et G. Perrin et J. Zerubia. Dans International Conference on Spatial Point Process Modelling and its Application (SPPA), Castellon, Spain, 2004.
Mots-clés : RJMCMC, Extraction d'objets, Processus ponctuels marques, Geometrie stochastique.
@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}, |
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= |
{ftp://ftp-sop.inria.fr/ariana/Articles/SPPA_2004.pdf}, |
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{RJMCMC, Extraction d'objets, Processus ponctuels marques, Geometrie stochastique} |
| } |
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 et X. Descombes et J. Zerubia et N. Baghdadi. Dans Proc. GRETSI Symposium on Signal and Image Processing, Paris, France, septembre 2003.
@INPROCEEDINGS{lacosteXDJZNB03,
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| author |
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{Lacoste, C. and Descombes, X. and Zerubia, J. and Baghdadi, N.}, |
| title |
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{Extraction de réseaux linéiques à partir d'images satellitaires par processus Markov objet}, |
| year |
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{2003}, |
| month |
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{septembre}, |
| booktitle |
= |
{Proc. GRETSI Symposium on Signal and Image Processing}, |
| address |
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{Paris, France}, |
| url |
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{http://documents.irevues.inist.fr/handle/2042/13529}, |
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|
6 - Road Network Extraction in Remote Sensing by a Markov Object Process.
C. Lacoste et X. Descombes et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Barcelona, Spain, septembre 2003.
@INPROCEEDINGS{lacosteXDJZ03,
|
| author |
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{Lacoste, C. and Descombes, X. and Zerubia, J.}, |
| title |
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{Road Network Extraction in Remote Sensing by a Markov Object Process}, |
| year |
= |
{2003}, |
| month |
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{septembre}, |
| booktitle |
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{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
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{Barcelona, Spain}, |
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{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1247420}, |
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{} |
| } |
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