ROTATION-INVARIANT NEURAL PATTERN-RECOGNITION SYSTEM WITH APPLICATION TO COIN RECOGNITION

被引：92
|
作者：
FUKUMI, M [1 ]
OMATU, S [1 ]
TAKEDA, F [1 ]
KOSAKA, T [1 ]
机构：
[1] GLORY LTD,HIMEJI 670,JAPAN
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 1992年 / 3卷 / 02期
关键词：
D O I：
10.1109/72.125868
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In pattern recognition, we must often deal with problems to classify a transformed pattern. In this paper, we propose a neural pattern recognition system which is insensitive to rotation of 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, we apply it to a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that our neural network approach works well for variable rotation pattern recognition problem.
引用
收藏
页码：272 / 279
页数：8
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