View-Based 3D Objects Recognition with Expectation Propagation Learning [chapter]

Adrien Bertrand, Faisal R. Al-Osaimi, Nizar Bouguila
2016 Lecture Notes in Computer Science  
View-based 3D Objects Recognition with Expectation Propagation Learning In this thesis, we present an improvement on the Expectation Propagation learning framework, specifically various enhancements on both speed and accuracy. We use this enhanced EP learning with the Inverted Dirichlet mixture model as well as the Dirichlet mixture model, to implement an algorithm to recognize 3D objects. Those objects are in our case from a view-based 3D models database that we have assembled. Following
more » ... ic rules determined by analyzing the results of our tests, we've been able to get good recognition rates. Experimental results are presented with different object classes by comparing recognition rates and confidence level, according to different tuning parameters we're able to refine towards specific classes for better specialized accuracy. iv ACKNOWLEDGMENTS First, I would like to thank my supervisor Dr. Nizar Bouguila, for the many hours spent with me at Concordia and on Skype, answering my questions and guiding me all along the way. Moreover, I want to express my gratitude to Dr. Taoufik Bdiri for giving me some pointers and a nice trick regarding an initialization algorithm.
doi:10.1007/978-3-319-50832-0_35 fatcat:gm4kbmutvrhw5ma2vusqkh5nr4