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

Mohammed Nazrul Islam; Md. Habibul Islam; K. Vijayan Asari; Mohammad A. Karim; Mohammad S. Alam

doi:10.1117/12.819530

13 April 2009 Distortion invariant pattern recognition using neural network based shifted phase-encoded joint transform correlation

Mohammed Nazrul Islam, Md. Habibul Islam, K. Vijayan Asari, Mohammad A. Karim, Mohammad S. Alam

Author Affiliations +

Proceedings Volume 7340, Optical Pattern Recognition XX; 734009 (2009) https://doi.org/10.1117/12.819530
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Mohammed Nazrul Islam, Md. Habibul Islam, K. Vijayan Asari, Mohammad A. Karim, and Mohammad S. Alam "Distortion invariant pattern recognition using neural network based shifted phase-encoded joint transform correlation", Proc. SPIE 7340, Optical Pattern Recognition XX, 734009 (13 April 2009); https://doi.org/10.1117/12.819530

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