The authors propose a pattern recognition system which is insensitive
to the rotation of the input pattern by various degrees. The system
consists of a fixed invariance network with many slabs and a trainable
multilayered network. To illustrate the effectiveness of the system,
the authors apply it to rotation-invariant coin recognition of 500
yen and 500 won coins. The results of computer simulation show that
a neural network approach will be useful in rotation-invariant pattern
recognition
%0 Conference Paper
%1 Fukumi1991
%A Fukumi, Minoru
%A Omatu, Sigeru
%A Takeda, Fumiaki
%A Kosaka, Toshihisa
%B Proceedings of the IEEE International joint Conference on Neural
Networks 1991, IJCNN'91
%D 1991
%I IEEE Computer Society
%K coin fixed invariance multilayered nets, network network, neural pattern recognition recognition, rotation-invariant system, trainable
%P 1027-1032
%R 10.1109/IJCNN.1991.170532
%T Rotation-invariant neural pattern recognition system with application
to coin recognition
%U http://ieeexplore.ieee.org/iel2/602/4411/00170532.pdf?tp=&arnumber=170532&isnumber=4411
%V 2
%X The authors propose a pattern recognition system which is insensitive
to the rotation of the input pattern by various degrees. The system
consists of a fixed invariance network with many slabs and a trainable
multilayered network. To illustrate the effectiveness of the system,
the authors apply it to rotation-invariant coin recognition of 500
yen and 500 won coins. The results of computer simulation show that
a neural network approach will be useful in rotation-invariant pattern
recognition
@inproceedings{Fukumi1991,
abstract = {The authors propose a pattern recognition system which is insensitive
to the rotation of the input pattern by various degrees. The system
consists of a fixed invariance network with many slabs and a trainable
multilayered network. To illustrate the effectiveness of the system,
the authors apply it to rotation-invariant coin recognition of 500
yen and 500 won coins. The results of computer simulation show that
a neural network approach will be useful in rotation-invariant pattern
recognition},
added-at = {2011-03-27T19:35:34.000+0200},
affiliation = {University of Tokushima, Faculty of Engineering, Department of Information
Science and Intelligent Systems},
author = {Fukumi, Minoru and Omatu, Sigeru and Takeda, Fumiaki and Kosaka, Toshihisa},
biburl = {https://www.bibsonomy.org/bibtex/23e8feab9cc5e8c7821ac40be769b37a5/cocus},
booktitle = {Proceedings of the IEEE International joint Conference on Neural
Networks 1991, IJCNN'91},
booktitleaddon = {July 8--12, 1991},
doi = {10.1109/IJCNN.1991.170532},
file = {:./fukumi1991_00170532.pdf:PDF},
interhash = {e8183a7b5208a9db8deb39c8fb953446},
intrahash = {3e8feab9cc5e8c7821ac40be769b37a5},
keywords = {coin fixed invariance multilayered nets, network network, neural pattern recognition recognition, rotation-invariant system, trainable},
location = {#ieeeaddr#},
owner = {CK},
pages = {1027-1032},
publisher = {{IEEE} Computer Society},
timestamp = {2011-03-27T19:35:39.000+0200},
title = {Rotation-invariant neural pattern recognition system with application
to coin recognition},
titleaddon = {\noop{}},
url = {http://ieeexplore.ieee.org/iel2/602/4411/00170532.pdf?tp=&arnumber=170532&isnumber=4411},
venue = {Seattle, WA, USA},
volume = 2,
volumes = {4},
xcrossref = {CK:conf/ijcnn/1991},
year = 1991
}