Optical Document Security: Measurement, Characterization and Visualization: in the Light of Visual Evaluation

Lindstrand, Mikael

doi:10.3384/diss.diva-151626

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…Gloss, an important parameter indicating the visual appearance of an object, is an optical property describing the ability of a surface to reect light into the specular direction. 32 Generally, the gloss values of materials are affected by various factors such as refractive index of the materials, the angle of incident light, and the surface topography. 33 Among them, the effect of surface topography on the gloss value can be determined using Rayleigh criteria.…”

Section: Resultsmentioning

confidence: 99%

Synthesis and characterization of self-cross-linkable and bactericidal methacrylate polymers having renewable cardanol moieties for surface coating applications

Choi

Kim

et al. 2014

RSC Adv.

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Section: Resultsmentioning

confidence: 99%

Synthesis and characterization of self-cross-linkable and bactericidal methacrylate polymers having renewable cardanol moieties for surface coating applications

Choi

Kim

et al. 2014

RSC Adv.

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“…Many investigations of gloss perception have been carried out with the purpose of good correlation between physical measurements and psychophysical assessments. Most of the studies focused on neutralcolored or texture-free samples, 8,13,14 but colorful surfaces with various textured impressions can be easily found in the automotive industry. It has also been demonstrated that the measured gloss data are not correlated satisfactorily with visually evaluated gloss values over the entire range of gloss levels 15 and so further studies are expected to analyze and scale gloss perception effectively.…”

Section: Introductionmentioning

confidence: 99%

Towards a practical metric of surface gloss for metallic coatings from automotive industry

Wang

et al. 2016

J Coat Technol Res

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The specular gloss of metallic coatings from the automotive industry can be well measured by current glossmeters when the geometry is appropriately chosen from 20°, 60°, and 85°based on the gloss levels observed. However, the measurements are discontinuous and even nonmonotonic at the breakpoints dividing samples into different gloss levels in the whole range, which causes confusion and inconvenience for industry applications. In this study, attempts were made to link the measurements at the three geometries to be continuous monotonically and in the meanwhile to improve the accuracy of estimating visual gloss. A psychophysical experiment based on a magnitude method was carried out to assess the gloss of metallic coatings with different colors and textures. The visually scaled gloss data were compared with spectrophotometric measurements and instrumental specular gloss values under different geometries. With different weighing coefficients for measurements at individual geometries depending on the gloss levels, a model was proposed to describe the visual surface gloss of metallic coatings comprehensively via a practical metric based on the geometry-dependent measurements. In this way, each sample would have a unique overall gloss value highly correlated with its perceptual evaluation over the entire gloss range.

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“…Psychophysics of gloss perception is a widely addressed topic in appearance sciences. [1][2][3] These classical studies usually cover a range of gloss variations (matt to mirrorlike) on uniform surfaces. The present study focuses on low gloss, achromatic, textured plastic surfaces.…”

Section: Introductionmentioning

confidence: 99%

Gloss evaluation and prediction of achromatic low‐gloss textured surfaces from the automotive industry

Passaro

Bidoret

Baron

et al. 2015

Color Research & Application

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International audienceIn this article, we investigate the benefit of including texture information in models of gloss perception of low-gloss textured achromatic plastic surfaces from the automotive industry. 4 models are compared: two gloss prediction models including texture information, one using data from reflectometry (M1) and one using data from goniophotometry (M2), and two models using data from reflectometry (M3) or goniophotometry (M4) alone. Both texture-corrected models (M1-M2) outclass the uncorrected intensity-based models, mainly because they are made texture invariant. Although the texture-corrected reflectometer-based prediction (M1) correlates rather well with sensory data, a more consistent fit is obtained by mixing textural to goniophotometric data (M2). This can be explained by the fact that contrast gloss is better than multiangle specular gloss at reflecting the observer's gloss evaluation strategy

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Optical Document Security: Measurement, Characterization and Visualization: in the Light of Visual Evaluation

Abstract: Visualization of the optical behavior of a diffractive Alphagram ® feature (€500 banknote). (c.f. Figure 5, page 28) Bringing a surface into a convex shape facilitates inspecting of e.g. gloss characteristics.

Cited by 4 publications

References 28 publications

Synthesis and characterization of self-cross-linkable and bactericidal methacrylate polymers having renewable cardanol moieties for surface coating applications

Synthesis and characterization of self-cross-linkable and bactericidal methacrylate polymers having renewable cardanol moieties for surface coating applications

Towards a practical metric of surface gloss for metallic coatings from automotive industry

Gloss evaluation and prediction of achromatic low‐gloss textured surfaces from the automotive industry

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