0578669 20240402214815.320231127235959.9 85176321042 001100953700004 10.2352/J.ImagingSci.Technol.2023.67.5.050408 9 s. P cav_un_epca*02583261062-370167 material appearance statistics image perception psychophysics cav_un_auth*0101086 Filip Jiří UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 50 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0458638 Vilímovská Veronika UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO 50 Ústav teorie informace a automatizace AV ČR, v. v. i. PDF http://library.utia.cas.cz/separaty/2023/RO/filip-0578669.pdf https://library.imaging.org/jist/articles/67/5/050408 GA22-17529S GA ČR CZ cav_un_auth*0439849 An efficient computational characterization of real-world materials is one of the challenges in image understanding. An automatic assessment of materials, with similar performance as human observer, usually relies on complicated image filtering derived from models of human perception. However, these models become too complicated when a real material is observed in the form of dynamic stimuli. This study tackles the challenge from the other side. First, we collected human ratings of the most common visual attributes for videos of wood samples and analyzed their relationship to selected image statistics. In our experiments on a set of sixty wood samples, we have found that such image statistics can perform surprisingly well in the discrimination of individual samples with reasonable correlation to human ratings. We have also shown that these statistics can be also effective in the discrimination of images of the same material taken under different illumination and viewing conditions. WOS BD 20000 20200 20201 2024 2 RVO:67985556 https://hdl.handle.net/11104/0347797 S 050408 Article Imaging Science Photographic Technology A IMAGINGSCIENCE&PHOTOGRAPHICTECHNOLOGY 0.8 0.6 7.2 0.00033 0.142 573 45 0.243 31.36 0.71 231 Q3 0.600 65 Q4 6.9 Q4 Q4 0.12 Q4 6.9 2023 85176321042 SCOPUS PUBMED 001100953700004 WOS PDF cav_un_epca*0258326 Journal of Imaging Science and Technology 67 5 2023 1062-3701 1943-3522