Neuro-pattern classification of elongated and contracted images

This paper presents a technique to classify images that have been elongated or contracted. The problem is formulated using conventional regular moments. It is shown that the conventional regular moment-invariants remain no longer invariant when the image is scaled unequally in the x- and y-directions. A method is proposed to form moment-invariants that do not change under such unequal scaling and shifting. By combining moments based on the theory of algebraic invariants, some of the features become rotation invariant. Results of computer simulations for images are also included, verifying the validity of the method proposed. The performance of a neural network to classify scaled, shifted, and rotated binary images is also reported.