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Assessment of sparkle and graininess in effect coatings using a high-resolution gonioreflectometer and psychophysical studies
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SYSNO ASEP 0545738 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Assessment of sparkle and graininess in effect coatings using a high-resolution gonioreflectometer and psychophysical studies Author(s) Filip, Jiří (UTIA-B) RID, ORCID
Vávra, Radomír (UTIA-B) RID, ORCID
Kolafová, Martina (UTIA-B)
Maile, F. J. (DE)Number of authors 4 Source Title Journal of Coatings Technology and Research. - : Springer - ISSN 1547-0091
Roč. 18, č. 6 (2021), s. 1511-1530Number of pages 20 s. Publication form Online - E Language eng - English Country US - United States Keywords Sparkle ; Graniness ; Psychophysics ; Gonioreflectometer Subject RIV IN - Informatics, Computer Science OECD category Robotics and automatic control R&D Projects GA17-18407S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000694798900009 EID SCOPUS 85114705645 DOI 10.1007/s11998-021-00518-5 Annotation The aim of this article is to propose a model to automatically predict visual judgement of sparkle and graininess of special effect pigments used in industrial coatings. Many applications in the paint and coatings, printing and plastics industry rely on multi-angle color measurements with the aim of properly characterizing the appearance, i.e., the color and texture of the manufactured surfaces. However, when it comes to surfaces containing effect pigments, these methods are in many cases insufficient and it is particularly texture characterization methods that are needed. There are two attributes related to texture that are commonly used: (1) diffuse coarseness or graininess and (2) sparkle or glint impression. In this paper, we analyzed visual perception of both texture attributes using two different psychophysical studies of 38 samples painted with effect coatings including different effect pigments and 31 test persons. Our previous work has shown a good agreement between a study using physical samples with one that uses high-resolution photographs of these sample surfaces. We have also compared the perceived (1) graininess and (2) sparkle with the performance of two commercial instruments that are capable of capturing both attributes. Results have shown a good correlation between the instruments’ readings and the psychophysical studies. Finally, we implemented computational models predicting these texture attributes that have a high correlation with the instrument readings as well as the psychophysical data. By linear scaling of the predicted data using instruments readings, one can use the proposed model for the prediction of graininess and both static and dynamic sparkle values. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022 Electronic address https://link.springer.com/article/10.1007/s11998-021-00518-5
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