Invariant pattern recognition of 2D images using neural networks and frequency-domain representation

Frequency domain representation of two dimensional gray-level images is used to develop a pattern recognition method that is invariant to rotation, translation and scaling. Frequency domain representation is a natural feature detector that allows the use of only few directions of highest energy as training data for a set of artificial neural networks (ANNs). We developed a new algorithm that uses the spectral information stored in these ANNs to compare a given image with a known pattern, determining the relative translation between them and yielding a measure of their similarity. The representation and method we adopted has the advantage of leaving only the rotation of the object as a free parameter to be determined by the algorithm. We minimize the spectral resolution noise using spectral directional filtering. Our experimental results indicate that the proposed method has excellent discriminating power.