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Efficient Monte Carlo sampler for detecting parametric objects in large scenes

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 e fficiency and competitiveness when addressing object recognition problems in vision. However, simulating these mathematical models is a diffi cult task, especially on large scenes. Existing samplers suff er 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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Submitted on : Wednesday, October 17, 2012 - 11:10:49 AM
Last modification on : Thursday, March 5, 2020 - 4:50:55 PM
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Yannick Verdie, Florent Lafarge. Efficient Monte Carlo sampler for detecting parametric objects in large scenes. ECCV 2012, Oct 2012, Firenze, Italy. pp.539-552, ⟨10.1007/978-3-642-33712-3_39⟩. ⟨hal-00742770⟩

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