0602524 20260226075726.520241209235959.9 85211954875 001565029100021 10.1007/978-3-031-78172-8_21 10 s. P cav_un_epca*0602523978-3-031-78172-80302-9743Lecture Notes in Computer Science324-333ChamSpringer Nature Switzerland2025AntonacopoulosApostolosChaudhuriSubhasisChellappaRamaLiuCheng-LinBhattacharyaSaumikPalUmapada Texture spectral quality comparison Texture modeling Texture synthesis Bidirectional texture function cav_un_auth*0101093 Haindl Michal 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*0283206 Havlíček Michal UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/RO/haindl-0602524.pdf We introduce texture spectral criteria, which allow us to predict whether simplified spectrally factorized random field-based texture models, a set of two-dimensional models, can faithfully replicate texture spectra compared to their fully spectrally correlated 3D counterparts. These probabilistic models incorporate essential two- or three-dimensional building factors for modeling the seven-dimensional Bidirectional Texture Function (BTF), the most advanced representation in real-world material visual properties modeling. While these models seamlessly approximate original measured massive data and extend them to arbitrary sizes or simulate unmeasured textures, evaluating typically involves time-consuming synthesis and psycho-physical evaluation. The proposed criteria provide an alternative approach, enabling us to bypass the spectral quality evaluation step. cav_un_auth*0478337 International Conference on Pattern Recognition 2024 /27./ 20241201 20241205 Kolkata IN BD 20000 20200 20202 2026 2 PO RVO:67985556 https://hdl.handle.net/11104/0360012 S 499 0.606 25.34 1.17 102124 Q2 2025 85211954875 SCOPUS PUBMED 001565029100021 WOS cav_un_epca*0602523 Pattern Recognition : 27th International Conference, ICPR 2024 Springer Nature Switzerland 2025 Cham 324 333 978-3-031-78172-8 Lecture Notes in Computer Science 15306 0302-9743 1611-3349 Antonacopoulos Apostolos 340 Chaudhuri Subhasis 340 Chellappa Rama 340 Liu Cheng-Lin 340 Bhattacharya Saumik 340 Pal Umapada 340