0561404 20230316105536.820220921235959.9 85140432803 000871953900019 10.1007/978-3-031-16210-7_19 13 s. P cav_un_epca*0561403978-3-031-16209-11865-0929237-249ChamSpringer International Publishing2022BadicaCostin Markovian Textural Features Scale Sensitivity Illumination Sensitivity cav_un_auth*0213290 Vácha Pavel UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Haindl Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition A Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2022/RO/vacha-0561404.pdf GA19-12340S GA ČR CZ cav_un_auth*0376011 Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the color histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample. cav_un_auth*0436572 International Conference on Computational Collective Intelligence (ICCCI 2022) /14./ 20220926 20220930 Hammamet TN BD 20000 20200 20205 2023 2 PR RVO:67985556 https://hdl.handle.net/11104/0334061 cav_un_auth*0295073 Vysoká škola ekonomická v Praze VŠE CZ S n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Interdisciplinary Applications|Computer Science Theory Methods 75 0.160 20.71 0.49 7773 Q4 2022 85140432803 SCOPUS PUBMED 000871953900019 WOS cav_un_epca*0561403 Advances in Computational Collective Intelligence : 14th International Conference, ICCCI 2022 Springer International Publishing 2022 Cham 237 249 978-3-031-16209-1 Communications in Computer and Information Science 1653 1865-0929 1865-0937 Badica Costin 340