This paper is concerned with the use of circular harmonic expansion for rotation invariant recognition, Mellin transform for scale invariant recognition and the combination of circular harmonic expansion with Mellin transform for rotation & scale invariant recognition. Distance measures between the reference image, or vector reference, and the test image are defined for comparison with a threshold for object recognition. In addition to providing the desired mathematical properties, the class of invariant recognition algorithms examined successfully detected the objects (targets) under different rotations and scale changes.
C. H. Chen,
"Study Of A Class Of Invariant Recognition For Machine Vision", Proc. SPIE 0707, Visual Communications and Image Processing, (20 November 1986); doi: 10.1117/12.937244; https://doi.org/10.1117/12.937244