0643905 20260225142355.220260105235959.9 11 s. E cav_un_epca*06439041613-0073GermanyCEUR-WS2025UrbanPhilippvon CastellChristoph FreiherrHardebergJon YngveFlemingRoland W.GigilashviliDavit Material Appearance Perceptual Attributes Visual Fingerprint Smartphone Application Material Retrieval cav_un_auth*0500278 Staš Adam UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO CZ 50 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0500279 Pilař Daniel UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO CZ 25 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101086 Filip Jiří UTIA-B Rozpoznávání obrazu Department of Pattern Recognition RO RO 25 K Ústav teorie informace a automatizace AV ČR, v. v. i. PDF 9 MB https://library.utia.cas.cz/separaty/2026/RO/filip-0643905.pdf GA22-17529S GA ČR CZ cav_un_auth*0439849 Assessment of material properties is essential for tasks such as similar material retrieval or swatch comparison in industrial design, manufacturing, and quality control. While many material similarity measures exist, they often fail to align with human perception. In this paper, we introduce a novel smartphone application using a machine learning model that leverages a perceptual representation known as the visual fingerprint of materials—linking image-based measurements to intuitive, human-understandable attributes. Trained on human ratings collected through psychophysical studies, the model can predict a material’s visual fingerprint using just two photographs captured under different lighting conditions. The application employ this model to assess any planar material sample using only a printed registration template and a flashlight. The app captures two photographs and predicts the material’s perceptual attributes. We demonstrate several practical use cases, including building personal material databases, retrieving visually similar materials, and exploring materials that match user-defined perceptual criteria. By enabling perceptually grounded comparisons and metadata extraction, our application provides a standardized representation of material appearance. This marks a step toward more intuitive and interoperable use of material properties across diverse digital environments. cav_un_auth*0500280 MANER 2025 20250629 20250629 Mainz/Darmstadt DE IN 20000 20200 20201 2026 3 PR RVO:67985556 https://hdl.handle.net/11104/0374431 S 4 2025 SCOPUS PUBMED WOS cav_un_epca*0643904 Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025) 1613-0073 Germany CEUR-WS 2025 Vol-4135 CEUR Workshop Proceedings 4135 Urban Philipp 340 von Castell Christoph Freiherr 340 Hardeberg Jon Yngve 340 Fleming Roland W. 340 Gigilashvili Davit 340