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Proceedings Paper

Distortion invariant pattern recognition using neural network based shifted phase-encoded joint transform correlation
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Paper Abstract

An optoelectronic neural network based detection technique is proposed for multi-class distortion-invariant pattern recognition. The neural network is utilized in the training stage for a sequence of multi-class binary and gray level images for supervised learning using shifted phase-encoded joint transform correlator with fringe adjusted filter in the hidden layer to create composite images that are invariant to distortion. Simulation results show that the proposed technique is efficient in recognizing targets in variable environmental conditions.

Paper Details

Date Published: 13 April 2009
PDF: 7 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 734009 (13 April 2009); doi: 10.1117/12.819530
Show Author Affiliations
Mohammed Nazrul Islam, Old Dominion Univ. (United States)
Md. Habibul Islam, Bangladesh Univ. of Engineering and Technology (Bangladesh)
K. Vijayan Asari, Old Dominion Univ. (United States)
Mohammad A. Karim, Old Dominion Univ. (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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