Rotation-invariant neural pattern recognition system estimating a rotation angle

In this paper a rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its rotation angle, is considered, To date, the authors have presented rotation-invariant neural pattern recognition systems. The recognition systems are effective for use in a rotated coin recognition problem, but their performance is still poor compared with human performance, It is well-known that humans sometimes recognize a rotated form by means of mental rotation, Such a fact, however, has never been considered in the design of neural pattern recognition systems, especially rotation-invariant systems, The occurrence of mental rotation can be explained in terms of the theory of information types, Therefore, we first examine the applicability of the theory to a rotation-invariant neural pattern recognition system. Next, we present a rotation-invariant neural network which can estimate a rotation angle, The neural network consists of a preprocessing network to detect the edge features of input patterns and a trainable multilayered network, Furthermore, a rotation-invariant neural pattern recognition system which includes the rotation-invariant neural network is proposed. This system is constructed on the basis of the above-mentioned theory, Finally, it is shown that, by means of computer simulations of a binary pattern and a coin recognition problem, the system is able to recognize rotated patterns and estimate their rotation angle.