0522528 20250310153222.820200226235959.9 000530845000012 85079864481 10.1016/j.patcog.2020.107264 11 s. P cav_un_epca*02573880031-3203103Elsevier Gaussian blur Semi-group Projection operator Blur invariants Image moments Affine transformation Combined invariants cav_un_auth*0336802 Department of Image Processing Kostková Jitka UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101087 Department of Image Processing Flusser Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0377447 Department of Image Processing Lébl Matěj UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0389933 Pedone M. FI http://library.utia.cas.cz/separaty/2020/ZOI/kostkova-0522528.pdf https://www.sciencedirect.com/science/article/pii/S0031320320300698 cav_un_auth*0360229 GA18-07247S GA ČR The paper presents a new theory of invariants to Gaussian blur. Unlike earlier methods, the blur kernel may be arbitrary oriented, scaled and elongated. Such blurring is a semi-group action in the image space, where the orbits are classes of blur-equivalent images. We propose a non-linear projection operator which extracts blur-insensitive component of the image. The invariants are then formally defined as moments of this component but can be computed directly from the blurred image without an explicit construction of the projections. Image description by the new invariants does not require any prior knowledge of the blur kernel parameters and does not include any deconvolution. The invariance property could be extended also to linear transformation of the image coordinates and combined affine-blur invariants can be constructed. Experimental comparison to three other blur-invariant methods is given. Potential applications of the new invariants are in blur/position invariant image recognition and in robust template matching. 2021 JD 10000 10200 10201 4 4 A sml 4as 2rh 20231122144807.8 2 R hod 20250310152230.7 20250310153222.8 RVO:67985556 http://hdl.handle.net/11104/0307360 1 S Assignment of Copyright 20200215 107264 2 Article Computer Science Artificial Intelligence|Engineering Electrical Electronic C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE|ENGINEERING.ELECTRICAL&ELECTRONIC 7.299 1.378 8.1 0.0327 1.761 33363 257 1.492 47.4 9.36 11364 Q1 6.615 413 Q1 89.179 90.5 Q1* Q1 1.96 Q1 92.857 2020 Soubory v repozitáři: kostkova-0522528.pdf, flusser-0522528-copyright.docx 85079864481 SCOPUS PUBMED 000530845000012 WOS cav_un_epca*0257388 Pattern Recognition 0031-3203 1873-5142 Roč. 103 č. 1 2020 Elsevier