An Adaptive Evidence Structure for Bayesian Recognition of 3D Objects

Classification of an object under various environmental conditions is a challenge for developing a reliable service robot. In this work, we show problems of using simple Naive Bayesian classifier and propose a Tree-Augmented Naive (TAN) Bayesian Network based classifier. We separate feature space into binary TRUE/FALSE regions which allows us to drive Bayesian inference prior conditional probabilities from statistical database. We go further using TRUE/FALSE regions to estimate expected posterior probabilities of each object under online specific conditions. These expectations are then used to select optimal feature sets under this environment and autonomously reconstruct Bayesian Network. Experimental results, validation and comparison show the performance of the proposed system.