13 April 2009 Distortion invariant pattern recognition using neural network based shifted phase-encoded joint transform correlation
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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.
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Mohammed Nazrul Islam, Mohammed Nazrul Islam, Md. Habibul Islam, Md. Habibul Islam, K. Vijayan Asari, K. Vijayan Asari, Mohammad A. Karim, Mohammad A. Karim, Mohammad S. Alam, 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); doi: 10.1117/12.819530; https://doi.org/10.1117/12.819530
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