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

Author

Listed:
  • Onsy A. Abdel Alim

    (Department of Electrical Engineering, Beirut University, Beirut, Lebanon)

  • Amin Shoukry

    (Computer Department, Egyptian Japanese University of Science and Technology (EJUST), Alexandria, Egypt)

  • Neamat A. Elboughdadly

    (Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt)

  • Gehan Abouelseoud

    (Department of Engineering, Alexandria University, Alexandria, Egypt)

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.

Suggested Citation

  • Onsy A. Abdel Alim & Amin Shoukry & Neamat A. Elboughdadly & Gehan Abouelseoud, 2013. "A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 2(2), pages 66-79, April.
    • Handle: RePEc:igg:jsda00:v:2:y:2013:i:2:p:66-79
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