  Geometric moments and moment invariants of image artifacts have many uses in
computer vision applications, e.g. shape classification or object position and
orientation. Higher order moments are of interest to provide additional feature
descriptors, to measure kurtosis or to resolve n-fold symmetry. This paper
provides the method and practical application to extend an efficient algorithm,
based on the Discrete Radon Transform, to generate moments greater than the 3rd
order. The mathematical fundamentals are presented, followed by relevant
implementation details. Results of scaling the algorithm based on image area
and its computational comparison with a standard method demonstrate the
efficacy of the approach.
