Abstract
We consider the use of Zernike moments (ZMs) for rotation- and scale-invariant classification of images. It is well known that ZMs are rotation-invariant only. We make use of the major benefit of the Fourier-Mellin (FM) transformation, which changes the rotation and the scale into translation. We introduce a new algorithm, which fuses the ZMs with the FM transform and is invariant under both rotation and scaling. Two sets of images were used to test the proposed algorithm. Experimental results reveal that the proposed algorithm has much better recognition rate than using ZMs within a variation of rotation between 0 and
, and scaling down and up between 25 and 400 of the original size.