0575788 20240402214441.920230925235959.9 10.1007/978-3-031-44237-7_28 11 s. P cav_un_epca*0575787978-3-031-44236-0295-305ChamSpringer2023TsapatsoulisN. 3D recognition 3D rotation invariants non-separable moments Appell polynomials cav_un_auth*0101087 Flusser Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101203 Suk Tomáš UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0426512 Bedratyuk L. UA cav_un_auth*0438860 Karella Tomáš UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/ZOI/flusser-0575788.pdf GA21-03921S GA ČR cav_un_auth*0412209 In this paper, we introduce new 3D rotation moment invariants, which are composed of non-separable Appell moments. The Appell moments can be substituted directly into the 3D rotation invariants instead of the geometric moments without violating their invariance. We show that non-separable moments may outperform the separable ones in terms of recognition power and robustness thanks to a better distribution of their zero surfaces over the image space. We test the numerical properties and discrimination power of the proposed invariants on three real datasets – MRI images of human brain, 3D scans of statues, and confocal microscope images of worms. cav_un_auth*0455368 Computer Analysis of Images and Patterns. CAIP 2023 20230925 20230928 Limassol CY JD 20000 20200 20206 2024 4 PR RVO:67985556 https://hdl.handle.net/11104/0345843 cav_un_auth*0455369 Khmelnytsky National University KNU UA S 2023 SCOPUS PUBMED WOS cav_un_epca*0575787 Computer Analysis of Images and Patterns. CAIP 2023 Springer 2023 Cham 295 305 978-3-031-44236-0 Lecture Notes in Computer Science 14184 Tsapatsoulis N. 340