Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes

International audience; 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.