Towards the parallelization of Reversible Jump Markov Chain Monte Carlo algorithms for vision problems

Yannick Verdie 1, 2 Florent Lafarge 1
1 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : Point processes have demonstrated efficiency and competitiveness when addressing object recognition problems in vision. However, simulating these mathematical models is a difficult task, especially on large scenes. Existing samplers suffer from average performances in terms of computation time and stability. We propose a new sampling procedure based on a Monte Carlo formalism. Our algorithm exploits Markovian properties of point processes to perform the sampling in parallel. This procedure is embedded into a data-driven mechanism such that the points are non-uniformly distributed in the scene. The performances of the sampler are analyzed through a set of experiments on various object recognition problems from large scenes, and through comparisons to the existing algorithms.
Type de document :
Rapport
[Research Report] RR-8016, INRIA. 2012
Liste complète des métadonnées

https://hal.inria.fr/hal-00720005
Contributeur : Florent Lafarge <>
Soumis le : lundi 23 juillet 2012 - 13:11:17
Dernière modification le : samedi 27 janvier 2018 - 01:31:38
Document(s) archivé(s) le : mercredi 24 octobre 2012 - 02:35:56

Fichier

RR-8016.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00720005, version 1

Collections

Citation

Yannick Verdie, Florent Lafarge. Towards the parallelization of Reversible Jump Markov Chain Monte Carlo algorithms for vision problems. [Research Report] RR-8016, INRIA. 2012. 〈hal-00720005〉

Partager

Métriques

Consultations de la notice

331

Téléchargements de fichiers

154