A new technique to derive invariant features for unequally scaled images

This paper presents a new technique to derive features for images that are translated, scaled equally/unequally and rotated. 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. The newly formed moments are also invariant to translation and reflection. However, it is not invariant for images that are rotated. A neural network is trained to estimate the angle of rotation; it is then used to derive the invariant moments for images that are unequally scaled, translated and rotated. Computer simulation results are also included to show the validity of the method proposed.