A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

Authors:
Onsy A. Abdel Alim;Amin Shoukry;Neamat A. Elboughdadly;Gehan Abouelseoud
Affiliations:
Department of Electrical Engineering, Beirut University, Beirut, Lebanon;Computer Department, Egyptian Japanese University of Science and Technology EJUST, Alexandria, Egypt;Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt;Department of Engineering, Alexandria University, Alexandria, Egypt
Venue:
International Journal of System Dynamics Applications
Year:
2013

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Abstract

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.