Share Email Print

Proceedings Paper

Artificial retinal neural network for visual pattern recognition
Author(s): Donghui Guo; Lee Ming Cheng; L. L. Cheng; Zhenxiang Chen; Ruitang Liu; Boxi Wu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

With feed-forward adaptive network (FFAN) and feed-back associative network (FBAN) respectively imitating the performances of retina and cerebral cortex, an artificial retinal neural network (ARNN) was presented in this paper for fast recognition of visual patterns. In our ARNN model to be implemented with neural network chip MD1200, every synaption of neurons can be arbitrarily given as an integer value from minus 215 to 215. After these synaptions are trained, the visual pattern not only under geometric transformation but also in the presence of noise can be recognized by the ARNN's system.

Paper Details

Date Published: 4 March 1996
PDF: 10 pages
Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234251
Show Author Affiliations
Donghui Guo, City Univ. of Hong Kong (China)
Lee Ming Cheng, City Univ. of Hong Kong (China)
L. L. Cheng, City Univ. of Hong Kong (China)
Zhenxiang Chen, Xiamen Univ. (China)
Ruitang Liu, Xiamen Univ. (China)
Boxi Wu, Xiamen Univ. (China)

Published in SPIE Proceedings Vol. 2664:
Applications of Artificial Neural Networks in Image Processing
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

© SPIE. Terms of Use
Back to Top