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Reports (Research Report) Year : 2012

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

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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.
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Dates and versions

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

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  • HAL Id : hal-00720005 , version 1

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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⟩
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