TY - GEN
T1 - Artificial retinal neural network for visual pattern recognition
AU - Guo, Donghui
AU - Cheng, L. M.
AU - Cheng, L. L.
AU - Chen, Zhenxiang
AU - Liu, Ruitang
AU - Wu, Boxi
PY - 1996
Y1 - 1996
N2 - 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 2 15 to 2 15. 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.
AB - 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 2 15 to 2 15. 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.
UR - http://www.scopus.com/inward/record.url?scp=0029713428&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0029713428&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0819420387
SN - 9780819420381
VL - 2664
SP - 153
EP - 162
BT - Proceedings of SPIE - The International Society for Optical Engineering
T2 - Applications of Artificial Neural Networks in Image Processing
Y2 - 1 February 1996 through 2 February 1996
ER -