Rotation-invariant neural pattern recognition system with application to coin recognition
Minoru Fukumi, Sigeru Omatu, Fumiaki Takeda, Tatsuro KOSAKA · IEEE Transactions on Neural Networks · 1992
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.