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Efficient Monte Carlo sampler for detecting parametric objects in large scenes

Yannick Verdie, Florent Lafarge
INRIA Sophia Antipolis, France
European Conference on Computer Vision (ECCV), 2012

@article{verdie2012efficient,

   title={Efficient Monte Carlo sampler for detecting parametric objects in large scenes},

   author={Verdi{‘e}, Y. and Lafarge, F.},

   year={2012}

}

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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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