An adaptive evidence structure for Bayesian recognition of 3D objects

Ahmed M. Naguib, Sukhan Lee
2015 Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication - IMCOM '15  
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 Naïve Bayesian classifier and propose a Tree-Augmented Naïve (TAN) Bayesian Networkbased 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
more » ... 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.
doi:10.1145/2701126.2701160 dblp:conf/icuimc/NaguibL14 fatcat:ewbhznhkpzhilccy63q2ll3t3m