Classification of Elongated and Contracted Images using New Regular Moments * P . RAVEENDRAN, * S. JEGANNATHAN, +SIGERU OMATU *Engineering Faculty Universiti Malaya Kuala Lumpur, 59100 Malaysia. This paper presents a technique to +Dept. of Information Science and Intelligent System Faculty of Engineering University of Tokushima Tokushima, 770, Japan. Abstract classify images that have been elongated or con- tracted. 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. Results of computer simulations for images are also included verifying the validity of the method proposed. 1. INTRODUCTION Ever since Hu [l] published his classic paper on pattern recognition using regular mo- ments, a lot of literature has appeared on this topic. Moments and functions of moments have been utilised as pattern features in a number of applications [2-31. These moments are very useful in recognising patterns that are scaled, shifted and rotated versions of the orig- inal image. However, when the image is an elongated or compressed version of the original image, as illustrated in Figs. (la) and (lb), these invariants no longer remain unchanged. In this paper, we form invariants that do not change under such unequal scaling in the z- and y-directions. The proposed invariants are also invariant under translation and mirror reflection. Their invariance to rotation is yet to be established. 0-7803-1901-X/94 $4.00 01994 IEEE -~ 4154