0454335 20240103211638.120160215235959.9 85016219716 000383905800023 10.1109/TIP.2015.2512108 17 s. P cav_un_epca*02532351057-7149252 (2016)790-806Institute of Electrical and Electronics Engineers Blurred image object recognition blur invariant comparison Gaussian blur projection operators image moments moment invariants cav_un_auth*0101087 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Flusser Jan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0317051 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Farokhi Sajad UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0282545 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Höschl Cyril UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101203 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Suk Tomáš UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101238 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Zitová Barbara UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0293889 Pedone M. IT http://library.utia.cas.cz/separaty/2016/ZOI/flusser-0454335.pdf cav_un_auth*0314467 GA15-16928S GA ČR CZ In this paper, we propose a new theory of invariants to Gaussian blur. We introduce a notion of a primordial image as a canonical form of all Gaussian blur-equivalent images. The primordial image is defined in spectral domain by means of projection operators. We prove that the moments of the primordial image are invariant to Gaussian blur and we derive recursive formulas for their direct computation without actually constructing the primordial image itself. We show how to extend their invariance also to image rotation. The application of these invariants is in blur-invariant image comparison and recognition. In the experimental part, we perform an exhaustive comparison with two main competitors: 1) the Zhang distance and 2) the local phase quantization. JD 2017 6 4 A hod 4ah 20231122141444.9 RVO:67985556 http://hdl.handle.net/11104/0257080 1 Department of Image Processing UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS S 1* Article Computer Science Artificial Intelligence|Engineering Electrical Electronic COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE|ENGINEERING.ELECTRICAL&ELECTRONIC 6.127 0.842 7 0.05273 1.826 29829 1.749 Q1 4.251 425 Q1 92.179 91.2 Q1* Q1* 91.794 2016 Soubory v repozitáři: flusser-0454335.pdf 85016219716 SCOPUS 000383905800023 WOS cav_un_epca*0253235 IEEE Transactions on Image Processing 1057-7149 1941-0042 Roč. 25 č. 2 2016 790 806 Institute of Electrical and Electronics Engineers