In pattern recognition, it is often necessary to deal with problems
to classify a transformed pattern. A neural pattern recognition system
which is insensitive to rotation of input pattern by various degrees
is proposed. The system consists of a fixed invariance network with
many slabs and a trainable multilayered network. The system was used
in a rotation-invariant coin recognition problem to distinguish between
a 500 yen coin and a 500 won coin. The results show that the approach
works well for variable rotation pattern recognition
%0 Journal Article
%1 Fukumi1992
%A Fukumi, Minoru
%A Omatu, Sigeru
%A Takeda, Fumiaki
%A Kosaka, Toshihisa
%C Los Alamitos, CA, USA
%D 1992
%I IEEE Computer Society
%J IEEE Transactions on Neural Networks
%K 500 coin coin, computerised fixed invariance multilayered nets500 network network, neural pattern recognition recognition, rotation-invariant system, trainable won yen
%N 2
%P 272-279
%R 10.1109/72.125868
%T Rotation-invariant neural pattern recognition system with application
to coin recognition
%U http://ieeexplore.ieee.org/iel4/72/3532/00125868.pdf?tp=&arnumber=125868&isnumber=3532
%V 3
%X In pattern recognition, it is often necessary to deal with problems
to classify a transformed pattern. A neural pattern recognition system
which is insensitive to rotation of input pattern by various degrees
is proposed. The system consists of a fixed invariance network with
many slabs and a trainable multilayered network. The system was used
in a rotation-invariant coin recognition problem to distinguish between
a 500 yen coin and a 500 won coin. The results show that the approach
works well for variable rotation pattern recognition
@article{Fukumi1992,
abstract = {In pattern recognition, it is often necessary to deal with problems
to classify a transformed pattern. A neural pattern recognition system
which is insensitive to rotation of input pattern by various degrees
is proposed. The system consists of a fixed invariance network with
many slabs and a trainable multilayered network. The system was used
in a rotation-invariant coin recognition problem to distinguish between
a 500 yen coin and a 500 won coin. The results show that the approach
works well for variable rotation pattern recognition},
added-at = {2011-03-27T19:47:06.000+0200},
address = {Los Alamitos, CA, USA},
author = {Fukumi, Minoru and Omatu, Sigeru and Takeda, Fumiaki and Kosaka, Toshihisa},
biburl = {https://www.bibsonomy.org/bibtex/272cc8128cf4518623f0691bf1c30e23a/cocus},
doi = {10.1109/72.125868},
file = {:./fukumi1992mar00125868.pdf:PDF},
interhash = {b8de816031eab7b2d4549c294609c169},
intrahash = {72cc8128cf4518623f0691bf1c30e23a},
issn = {1045-9227},
journal = {{IEEE} Transactions on Neural Networks},
keywords = {500 coin coin, computerised fixed invariance multilayered nets500 network network, neural pattern recognition recognition, rotation-invariant system, trainable won yen},
month = mar,
number = 2,
owner = {CK},
pages = {272-279},
publisher = {{IEEE} Computer Society},
timestamp = {2011-03-27T19:47:08.000+0200},
title = {Rotation-invariant neural pattern recognition system with application
to coin recognition},
url = {http://ieeexplore.ieee.org/iel4/72/3532/00125868.pdf?tp=&arnumber=125868&isnumber=3532},
urldate = {25-1-2008},
volume = 3,
year = 1992
}