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View-based 3D Objects Recognition with Expectation Propagation Learning


View-based 3D Objects Recognition with Expectation Propagation Learning

Bertrand, Adrien (2016) View-based 3D Objects Recognition with Expectation Propagation Learning. Masters thesis, Concordia University.

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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 specific 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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Bertrand, Adrien
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information Systems Security
Date:March 2016
Thesis Supervisor(s):Bouguila, Nizar
ID Code:981025
Deposited On:15 Jun 2016 16:28
Last Modified:18 Jan 2018 17:52


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