Efficient Computation of Higher Order 2D Image Moments using the Discrete Radon Transform
This work addresses a domain-specific need in computer vision for more efficient feature extraction in shape classification and object analysis, but it is incremental as it builds on an existing algorithm.
The paper tackles the problem of efficiently computing higher-order 2D image moments beyond the 3rd order by extending an algorithm based on the Discrete Radon Transform, resulting in demonstrated efficacy through computational comparisons with a standard method.
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.