Rotation and scaling invariant self-organizing mapping

Abstract

Invariant scaling and rotation recognition of an image has been successfully realized by extracting the features of the image based on various techniques such as moment, e.g., the Zernike moment, pulsed coupled neural network, and high order neural network. These approaches are costly in terms of computational time and network complexity. They are not practical when applied with an image of size at least 256 /spl times/ 256 pixels. In this paper, we reduce these complexities by applying the capability of a self-organizing mapping network such as Kohonen's competitive learning to extract the features. However, the competitive learning cannot be directly applied to this invariant scaling and rotation recognition problem. Some learning modifications are proposed so that no matter how an image is scaled or rotated the location of each neuron is always at the same coordinates with respect to its neighboring neurons. The new competitive learning was successfully tested with gray-scaled images.