|
|
Publications de Charpiat G.
Résultat de la recherche dans la liste des publications :
Article |
1 - 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. |
|
 haut de la page
Article de conférence |
1 - A fast multiple birth and cut algorithm using belief propagation.
A. Gamal Eldin et X. Descombes et Charpiat G. et J. Zerubia. Dans Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, septembre 2011.
Mots-clés : Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation.
@INPROCEEDINGS{MBC_ICIP11,
|
| author |
= |
{Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, |
| title |
= |
{A fast multiple birth and cut algorithm using belief propagation}, |
| year |
= |
{2011}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Brussels, Belgium}, |
| url |
= |
{http://hal.inria.fr/inria-00592446/fr/}, |
| keyword |
= |
{Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation} |
| } |
Abstract :
In this paper, we present a faster version of the newly proposed Multiple Birth and Cut (MBC) algorithm. MBC is an optimization method applied to the energy minimization of an object based model, defined by a marked point process. We show that, by proposing good candidates in the birth step of this algorithm, the speed of convergence is increased. The algorithm starts by generating a dense configuration in a special organization, the best candidates are selected using the belief propagation algorithm. Next, this candidate configuration is combined with the current configuration using binary graph cuts as presented in the original version of the MBC algorithm. We tested the performance of our algorithm on the particular problem of counting flamingos in a colony, and show that it is much faster with the modified birth step. |
|
haut de la page
Ces pages sont générées par 
|
Rapport d'activité 2011
bibtex
