MatTag: Practical material tagging using visual fingerprints

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00643905" target="_blank" >RIV/67985556:_____/25:00643905 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

MatTag: Practical material tagging using visual fingerprints

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

MatTag: Practical material tagging using visual fingerprints

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/GA22-17529S" target="_blank" >GA22-17529S: Vizuální identifikátor vzhledu materiálů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the MANER Conference Mainz/Darmstadt 2025 (MANER 2025)

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

11

Strana od-do

4

Název nakladatele

CEUR-WS

Místo vydání

Germany

Místo konání akce

Mainz/Darmstadt

Datum konání akce

29. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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