Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes

  • Yannick Verdié
  • Florent Lafarge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yannick Verdié
    • 1
  • Florent Lafarge
    • 1
  1. 1.INRIA Sophia AntipolisFrance

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