Rotated coin recognition using neural networks

Abstract

Neural networks have been used in the development of intelligent recognition systems that simulate our ability recognize patterns. However, rotated objects may cause incorrect identification by recognition systems. Our quick glance provides an overall approximation of a pattern regardless of noise or rotations. This paper proposes that the overall approximation of a pattern can be achieved via pattern averaging prior to training a neural network to recognize that pattern in various rotations. Pattern averaging provides the neural network with "fuzzy" rather than "crisp" representations of the rotated objects, thus, minimizing computational costs and providing the neural network with meaningful learning of various rotations of an object. The proposed method will be used to recognize rotated coins and is implemented to solve an existing problem where slot machines in Europe accept the new Turkish 1 Lira coin as a 2 Euro coin. © 2007 Springer-Verlag Berlin Heidelberg.