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Laurent Garcin
Former Master Student, Ecole Polytechnique / IGN, DEA
 Contact :
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| Last publications in Ariana Research Group :
Building extraction using a Markov point process.
L. Garcin and X. Descombes and J. Zerubia and H. Le Men. In Proc. IEEE International Conference on Image Processing (ICIP), papier invité, Thessalonique, Grèce, October 2001.
@INPROCEEDINGS{xd01d,
|
| author |
= |
{Garcin, L. and Descombes, X. and Zerubia, J. and Le Men, H.}, |
| title |
= |
{Building extraction using a Markov point process}, |
| year |
= |
{2001}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{papier invité, Thessalonique, Grèce}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=958555}, |
| keyword |
= |
{} |
| } |
Building detection by markov object processes and a MCMC algorithm.
L. Garcin and X. Descombes and J. Zerubia and H. Le Men. Research Report 4206, Inria, France, June 2001.
Keywords : Stochastic geometry, Marked point process, Buildings, RJMCMC.
@TECHREPORT{xd01a,
|
| author |
= |
{Garcin, L. and Descombes, X. and Zerubia, J. and Le Men, H.}, |
| title |
= |
{Building detection by markov object processes and a MCMC algorithm}, |
| year |
= |
{2001}, |
| month |
= |
{June}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{4206}, |
| address |
= |
{France}, |
| url |
= |
{https://hal.inria.fr/inria-00072416}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72416/filename/RR-4206.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/24/16/PS/RR-4206.ps}, |
| keyword |
= |
{Stochastic geometry, Marked point process, Buildings, RJMCMC} |
| } |
Résumé :
Le but de ce travail est de détecter les bâtiments à partir de photographies aeriennes numériques. Nous modélisons un ensemble de bâtiments par une configuration d'objets. Nous définissons un processus ponctuel sur l'ensemble des configurations qui se décompose en deux parties :
* La première est un modèle a priori sur les configurations qui considère des interactions entre les objets,
* la seconde est un modèle d'attache aux données qui induit la cohérence du résultat avec l'image traitée.
Nous avons ainsi une distribution a posteriori dont nous recherchons la configuration maximale. Pour obtenir ce maximum, nous utilisons une simulatio- n de type MCMC - un algorithme de Metropolis-Hasting-Green- couplée avec un schéma de recuit simulé. Nous testons la méthode décrite à la fois sur des données synthétiques et des images stéréoscopiques réelles. |
Abstract :
This work aims at detecting buildings in digital aerial photographs. Here we model a set of buildings by a configuration of objects. We define a point process on the set of configurations, which splits into two parts :
* the first one is a prior model on the configurations which use interactions between objects,
* the second one is a data model which enforces the coherence with the image.
Thus we have a posterior distribution whose maximum has to be found. In order to achieve this maximum, we use a MCMC simulation - a Metropolis-Hasting- s-Green algorithm - mixed with a simulated annealing. Then we test this method on both synthetic and real stereo-images. |
A RJMCMC algorithm for object processes in image processing.
X. Descombes and R. Stoica and L. Garcin and J. Zerubia. Monte Carlo Methods and Applications, 7(1-2): pages 149-156, 2001.
@ARTICLE{xd01c,
|
| author |
= |
{Descombes, X. and Stoica, R. and Garcin, L. and Zerubia, J.}, |
| title |
= |
{A RJMCMC algorithm for object processes in image processing}, |
| year |
= |
{2001}, |
| journal |
= |
{Monte Carlo Methods and Applications}, |
| volume |
= |
{7}, |
| number |
= |
{1-2}, |
| pages |
= |
{149-156}, |
| url |
= |
{http://www.degruyter.com/view/j/mcma.2001.7.issue-1-2/mcma.2001.7.1-2.149/mcma.2001.7.1-2.149.xml}, |
| keyword |
= |
{} |
| } |
|
All publications in Ariana Research Group
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2011 Activity report
