9 November 1993 Optoelectronically implemented neural network with a wavelet preprocessor
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Abstract
An optoelectronic neural network based upon the Neocognitron paradigm has been implemented at JPL and successfully demonstrated for automatic target recognition for both focal plane array imageries and range-Doppler radar signatures. A novel feature of this neural network architectural design is the use of a shift-invariant multichannel Fourier optical correlation as a building block for iterative multilayer processing. An innovative bipolar neural weights holographic synthesis technique was utilized to implement both the excitatory and inhibitory neural functions and dramatically increase its discrimination capability. In order to further increase the optoelectronic Neocognitron's self-organization processing ability, a wavelet preprocessor has been developed for feature extraction preprocessing (orientation, size, location, etc.). The addition of this wavelet processor would enable the neocognitron to dynamically focus on the incoming targets based on their known features and result in higher discrimination and lower false alarm rate. The theoretical analysis of an orientation and scale selective wavelet is provided. A multichannel optoelectronic wavelet processor using an e- beam complex-valued wavelet filter is also presented. Experimental demonstrations of wavelet preprocessing for feature extraction are also provided.
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Tien-Hsin Chao, Tien-Hsin Chao, Eric R. Hegblom, Eric R. Hegblom, Brian Lau, Brian Lau, William W. Stoner, William W. Stoner, William J. Miceli, William J. Miceli, } "Optoelectronically implemented neural network with a wavelet preprocessor", Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993); doi: 10.1117/12.163596; https://doi.org/10.1117/12.163596
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