Parts-based segmentation with overlapping part models using Markov chain Monte Carlo
Image and Vision Computing
Detection and localization of the top object in the stack of objects
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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A statistically optimal formulation is presented for recognizing multiple, partially occluded objects. The optimality, in terms of the maximum a posteriori (MAP) principle, is with respect to all, rather than just individual modeled objects. Various constraints are incorporated into the posterior distribution, a two-stage MAP estimation approach is proposed to reduce the computational cost