Portable holographic neural system for distortion-invariant pattern recognition and tracking in controlled environment

Andrew A. Kostrzewski; Thomas Taiwei Lu; Hung Chou; Freddie Shing-Hong Lin

doi:10.1117/12.138339

1 July 1992 Portable holographic neural system for distortion-invariant pattern recognition and tracking in controlled environment

Andrew A. Kostrzewski, Thomas Taiwei Lu, Hung Chou, Freddie Shing-Hong Lin

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Proceedings Volume 1701, Optical Pattern Recognition III; (1992) https://doi.org/10.1117/12.138339
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

We present a novel neuro-optic system for ATR based on an advanced holographic technique. This system has a spatially recorded high density holographic matrix which works in conjunction with a 2-D spatial light modulator to perform highly parallel inter-pixel operations. Because of the high degree of parallel processing necessary for real-time performance and the need for high interconnectivity and large memory capacity, an optical neural network approach to ATR fulfills more than the basic requirements for an ATR system and has the potential to outperform, or at a minimum supplement, an electronic neural network approach. Our neural network is based on inter-pattern association model and interconnection network is implemented using N⁴ hologram recording. In this paper, computer simulation as well as optical implementation of the neural network is presented. Computer simulations is used to implement neural network with 64 X 64 neurons for rotation invariance and weak scale invariance in a controlled environment. For the optical implementation we used 32 X 32 fully interconnected neural network to test noise robustness.

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Andrew A. Kostrzewski, Thomas Taiwei Lu, Hung Chou, and Freddie Shing-Hong Lin "Portable holographic neural system for distortion-invariant pattern recognition and tracking in controlled environment", Proc. SPIE 1701, Optical Pattern Recognition III, (1 July 1992); https://doi.org/10.1117/12.138339

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KEYWORDS

Neural networks

Image processing

Holography

Neurons

Automatic target recognition

Optical pattern recognition

Signal to noise ratio

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