Material fingerprinting: predicting human perception of material appearance through psychophysical analysis and neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00641338" target="_blank" >RIV/67985556:_____/25:00641338 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68081740:_____/25:00641338

Výsledek na webu

<a href="https://royalsocietypublishing.org/rsos/article/12/11/250513/234224/Material-fingerprinting-predicting-human" target="_blank" >https://royalsocietypublishing.org/rsos/article/12/11/250513/234224/Material-fingerprinting-predicting-human</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1098/rsos.250513" target="_blank" >10.1098/rsos.250513</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Material fingerprinting: predicting human perception of material appearance through psychophysical analysis and neural networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Material fingerprinting: predicting human perception of material appearance through psychophysical analysis and neural networks

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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 periodika

Royal Society Open Science

ISSN

2054-5703

e-ISSN

2054-5703

Svazek periodika

12

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

250513

Kód UT WoS článku

001618124500012

EID výsledku v databázi Scopus

2-s2.0-105021299046