{"title":"Discrete Radial-Harmonic-Fourier Moments for Image Description","authors":"Kejia Wang, Ziliang Ping, Y. Sheng","doi":"10.1145/3589572.3589600","DOIUrl":null,"url":null,"abstract":"A new type of multi-distorted invariant discrete orthogonal moments, discrete Radial-Harmonic-Fourier moments was proposed. The kernel function of the moments was composed of radial discrete orthogonal triangular function and angular Fourier complex componential factor. The relationship between discrete Radial-Harmonic-Fourier moments and Radial-Harmonic-Fourier moments was also analyzed. The experimental results indicate that the discrete Radial-Harmonic-Fourier moments have excellent image description ability and can be effectively used as invariant image features in image analysis and pattern recognition.","PeriodicalId":296325,"journal":{"name":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 6th International Conference on Machine Vision and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3589572.3589600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A new type of multi-distorted invariant discrete orthogonal moments, discrete Radial-Harmonic-Fourier moments was proposed. The kernel function of the moments was composed of radial discrete orthogonal triangular function and angular Fourier complex componential factor. The relationship between discrete Radial-Harmonic-Fourier moments and Radial-Harmonic-Fourier moments was also analyzed. The experimental results indicate that the discrete Radial-Harmonic-Fourier moments have excellent image description ability and can be effectively used as invariant image features in image analysis and pattern recognition.