bibtype C - Conference Paper (international conference)
ARLID 0643905
utime 20260115100715.5
mtime 20260105235959.9
title (primary) (eng) MatTag: Practical material tagging using visual fingerprints
specification
page_count 11 s.
media_type E
serial
ARLID cav_un_epca*0643904
ISSN 1613-0073
title Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025)
publisher
place Germany
name CEUR-WS
year 2025
editor
name1 Urban
name2 Philipp
editor
name1 von Castell
name2 Christoph Freiherr
editor
name1 Hardeberg
name2 Jon Yngve
editor
name1 Fleming
name2 Roland W.
editor
name1 Gigilashvili
name2 Davit
keyword Material Appearance
keyword Perceptual Attributes
keyword Visual Fingerprint
keyword Smartphone Application
keyword Material Retrieval
author (primary)
ARLID cav_un_auth*0500278
name1 Staš
name2 Adam
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept (eng) Department of Pattern Recognition
department (cz) RO
department (eng) RO
country CZ
share 50
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0500279
name1 Pilař
name2 Daniel
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
country CZ
share 25
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101086
name1 Filip
name2 Jiří
institution UTIA-B
full_dept (cz) Rozpoznávání obrazu
full_dept Department of Pattern Recognition
department (cz) RO
department RO
share 25
garant K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
source_type PDF
source_size 9 MB
url https://library.utia.cas.cz/separaty/2026/RO/filip-0643905.pdf
cas_special
project
project_id GA22-17529S
agency GA ČR
country CZ
ARLID cav_un_auth*0439849
abstract (eng) 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.
action
ARLID cav_un_auth*0500280
name MANER 2025
dates 20250629
mrcbC20-s 20250629
place Mainz/Darmstadt
country DE
RIV IN
FORD0 20000
FORD1 20200
FORD2 20201
reportyear 2026
num_of_auth 3
presentation_type PR
inst_support RVO:67985556
permalink https://hdl.handle.net/11104/0374431
confidential S
article_num 4
arlyear 2025
mrcbU14 SCOPUS
mrcbU24 PUBMED
mrcbU34 WOS
mrcbU63 cav_un_epca*0643904 Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025) 1613-0073 0074-4135 Germany CEUR-WS 2025 Vol-4135 CEUR Workshop Proceedings 4135
mrcbU67 Urban Philipp 340
mrcbU67 von Castell Christoph Freiherr 340
mrcbU67 Hardeberg Jon Yngve 340
mrcbU67 Fleming Roland W. 340
mrcbU67 Gigilashvili Davit 340