Towards the parallelization of Reversible Jump Markov Chain Monte Carlo algorithms for vision problems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2012

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

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.
Fichier principal
Vignette du fichier
RR-8016.pdf (2.99 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-00720005 , version 1 (23-07-2012)

Identifiers

  • HAL Id : hal-00720005 , version 1

Cite

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⟩
189 View
111 Download

Share

Gmail Mastodon Facebook X LinkedIn More