Characteristic recognition system with artificial neural network

Shizhe Shang; Jun Liu; Yunshen Zhang; Yan Piao

doi:10.1117/12.252930

3 October 1996 Characteristic recognition system with artificial neural network

Shizhe Shang, Jun Liu, Yunshen Zhang, Yan Piao

Proceedings Volume 2897, Electro-Optic and Second Harmonic Generation Materials, Devices, and Applications; (1996) https://doi.org/10.1117/12.252930
Event: Photonics China '96, 1996, Beijing, China

Abstract

The optical recognition system is based on the optical characteristic extractor. In this report, a kind of new theory of the characteristic recognition system with artificial neural network is introduced. The optical compound eye system, lateral inhibition network and back propagation network (BP) are adopted to form a parallel neural network, recognition system. The field of view is divided into mosaic pixels by the plane compound eye lens, which is convenient to use single photoelectric detector. The information received by the detector is extracted characteristic through the lateral inhibition network. It is a parallel neural network made up of resistor network and it has the advantage of high speed, simple structure, etc. BP network is used for pattern recognition. Its weights are anew distributed during network learning processing. Once the studied object is detected again, the system will quickly response its pattern. In this paper, several experimental data of simple patters are given, and the precessions of the network recognition are analyzed. Finally, it is pointed out that the characteristic recognition system is feasible in applying to industrial detection and Chinese character recognition, etc.

Citation Download Citation

Shizhe Shang, Jun Liu, Yunshen Zhang, and Yan Piao "Characteristic recognition system with artificial neural network", Proc. SPIE 2897, Electro-Optic and Second Harmonic Generation Materials, Devices, and Applications, (3 October 1996); https://doi.org/10.1117/12.252930

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KEYWORDS

Pattern recognition

Artificial neural networks

Neural networks

Sensors

Eye

Optical networks

Optical pattern recognition

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