2020/09/04 by William Diggin, Diggin, William, Michael Diggin +1
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.2009.09898
arxiv created 2020/09/04 · openalex publication_date 2020/09/04 · arxiv updated 2020/09/22 · openalex created_date 2020/10/01 · openalex updated_date 2026/07/28
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.