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 360 deg, and
scaling down and up between 25% and 400% of the original size.