0641338 20260224163925.420251112235959.9 105021299046 001618124500012 10.1098/rsos.250513 21 s. E cav_un_epca*04754082054-570312Royal Society Publishing material perception feature predicting neural network fingerprint cav_un_auth*0101086 Filip Jiří UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 40 K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0294719 Děchtěrenko Filip PSU-E Kognitivní psychologie Department of Cognitive Psychology Institute of psychology CZ 10 Psychologický ústav AV ČR, v. v. i. cav_un_auth*0340873 Schmidt F. DE 10 cav_un_auth*0106408 Lukavský Jiří PSU-E Kognitivní psychologie Department of Cognitive Psychology Institute of psychology CZ 10 Psychologický ústav 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 10 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 CZ 10 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0468180 Fleming R. W. DE 10 https://library.utia.cas.cz/separaty/2025/RO/filip-0641338.pdf https://royalsocietypublishing.org/rsos/article/12/11/250513/234224/Material-fingerprinting-predicting-human GA22-17529S GA ČR CZ cav_un_auth*0439849 Digital representation of materials is crucial in fields such as virtual reality, industrial design and quality control. However, predicting human perception of materials from image data is challenging due to the complexity of material appearances and the intricacies of human vision. This study introduces a perceptual representation termed the ‘visual fingerprint’, linking image-based measurements of materials to intuitive, human-understandable attributes. We conducted psychophysical studies using standardized video sequences of 347 diverse real-world materials, including fabrics and wood, selected to encompass a broad spectrum of textures, colours and reflective properties. Sixteen key appearance attributes were identified, and over 110 000 human ratings were collected to map perceptual attributes across material categories. By integrating CLIP-derived image features with a multi-layer perceptron model, we developed a predictive framework for material perception. Our results demonstrate that human judgements of appearance and similarity can be accurately predicted using only two images of a material. This work offers a practical and interpretable approach to material representation, enabling intuitive comparisons and retrievals in applications where material appearance is crucial. The proposed material fingerprint and its prediction directly from image data represent a significant step towards simplifying the understanding and interoperability of material properties in diverse digital environments. WOS BD 10000 10200 10201 2026 7 PSU-E 50000 50100 50101 PSU-E AN RVO:67985556 RVO:68081740 https://hdl.handle.net/11104/0371959 cav_un_auth*0354022 Justus Liebig University Giessen DE S 250513 A MULTIDISCIPLINARYSCIENCES 3.2 0.6 5.3 0.0227 0.922 17898 92 0.795 67.64 2.99 5847 Q1 2.800 642 Q2 73.2 0.9 Q1 73.2 2025 105021299046 SCOPUS 41244060 PUBMED 001618124500012 WOS cav_un_epca*0475408 Royal Society Open Science 12 11 2025 2054-5703 2054-5703 Royal Society Publishing