0476980 20240103214354.120170814235959.9 85028456746 000432084600024 10.1007/978-3-319-64698-5_24 11 s. P cav_un_epca*0477026978-3-319-64698-5Part II285-295ChamSpringer2017FelsbergM.HeydenA.KrügerN. Affine invariants Image moments Feature selection Machine learning Pattern recognition cav_un_auth*0293261 Zita Aleš 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*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*0293863 Kotera Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/ZOI/zita-0476980.pdf cav_un_auth*0314467 GA15-16928S GA ČR cav_un_auth*0348578 1094216 GA UK CZ Moment invariants are one of the techniques of feature extraction frequently used for pattern recognition algorithms. A moment is a projection of function into polynomial basis and an invariant is a function returning the same value for an input with and without particular class of degradation. Several techniques of moment invariant creation exist often generating over-complete set of invariants. Dependencies in these sets are commonly in a form of complicated polynomials, further- more they can contain dependencies of higher orders. These theoretical dependencies are valid in the continuous domain but it is well known that in discrete cases are often invalidated by discretization. Therefore, it would be feasible to begin classi cation with such an over-complete set and adaptively nd the pseudo-independent set of invariants by the means of feature selection techniques. This study focuses on testing of the infuence of theoretical invariant dependencies in discrete pattern recognition applications. cav_un_auth*0348547 International Conference on Computer Analysis of Images and Patterns (CAIP 2017) /17./ 20170822 20170824 Ystad SE JD 20000 20200 20205 2018 4 PR RVO:67985556 http://hdl.handle.net/11104/0273427 S RIV/67985556:_____/17:00476980!RIV18-AV0-67985556 191975666 Doplnění UT WOS RIV/67985556:_____/17:00476980!RIV18-GA0-67985556 191965017 Doplnění UT WOS n.a. Proceedings Paper Computer Science Theory Methods|Imaging Science Photographic Technology n.a. Proceedings Paper Computer Science Theory Methods|Imaging Science Photographic Technology n.a. Proceedings Paper Computer Science Theory Methods|Imaging Science Photographic Technology 2017 85028456746 SCOPUS PUBMED 000432084600024 WOS cav_un_epca*0477026 Computer Analysis of Images and Patterns : 17th International Conference, CAIP 2017 Part II 978-3-319-64698-5 285 295 Cham Springer 2017 Lecture Notes in Computer Science LNCS 10425 Felsberg M. 340 340 Heyden A. 340 Krüger N.