Probabilistic 3D object recognition with both positive and negative evidences

In real applications, sometimes, visual recognition may need to rely on incomplete or ambiguous features for a unique decision. Furthermore, the detected features may suffer a lot of uncertainties due to environment changes. In order to solve the problem with ambiguities and uncertainties in one computational framework, we propose a probabilistic 3D object recognition approach using both positive and negative evidences in cluttered environment. First of all, initial feature are selected as parallel and perpendicular line pairs to generate pose hypotheses as the multiple interpretations. Secondly, given a 3D polyhedral object model and the estimated pose, positive and negative evidences can be identified as additional information for probability computation of the multiple interpretations. More specifically, given the estimated pose, followed by visibility test, positive evidence is the feature that should be appeared around the pose, and negative evidence is the feature that should not be appeared due to self-occlusion. Where the probability is computed using Bayesian principle in terms of both likelihood and unlikelihood. The experimental results support the potential of the proposed approach in the real environment.