New invariant moments for non-uniformly scaled images

The usual regular moment functions are only invariant to image translation, rotation and uniform scaling. These moment invariants are not invariant wh en an image is scaled non-uniformly in the x and y-axes directions. This pa per addresses this problem by presenting a new technique to obtain moments that are invariant to non uniform scaling. However, this technique produces a set of features that are only invariant to translation and uniform/non-u niform scaling. To obtain invariance to rotation, moments are calculated wi th respect to the x-y-axis of the image. To perform this, a neural network is used to estimate the angle of rotation from the x-y-axis and the image i s unrotated to the x-y axis. Consequently, we are able to obtain features t hat are invariant to translation, rotation and uniform/non-uniform scaling. The mathematical background behind the development and invariance of the n ew moments are presented. The results of experimental studies using English alphabets and Arabic numerals scaled uniformly/non-uniformly, rotated and translated are discussed to further verify the validity of the new moments.