MatMix 1.0

Using optical mixing to probe visual material perception

Journal Article (2016)
Author(s)

Fan Zhang (TU Delft - Industrial Design Engineering)

Huib de Ridder (TU Delft - Industrial Design Engineering)

Ronald W. Fleming (Justus Liebig University Giessen)

Sylvia Pont (TU Delft - Industrial Design Engineering)

Research Group
Human Information Communication Design
DOI related publication
https://doi.org/10.1167/16.6.11 Final published version
More Info
expand_more
Publication Year
2016
Language
English
Research Group
Human Information Communication Design
Issue number
6
Volume number
16
Pages (from-to)
1-18
Downloads counter
268
Collections
Institutional Repository
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

MatMix 1.0 is a novel material probe we developed for quantitatively measuring visual perception of materials. We implemented optical mixing of four canonical scattering modes, represented by photographs, as the basis of the probe. In order to account for a wide range of materials, velvety and glittery (asperity and mesofacet scattering) were included besides the common matte and glossy modes (diffuse and forward scattering). To test the probe, we conducted matching experiments in which inexperienced observers were instructed to adjust the modes of the probe to match its material to that of a test stimulus. Observers were well able to handle the probe and match the perceived materials. Results were robust across individuals, across combinations of materials, and across lighting conditions. We conclude that the approach via canonical scattering modes and optical mixing works well, although the image basis of our probe still needs to be optimized. We argue that the approach is intuitive, since it combines key image characteristics in a ''painterly'' approach. We discuss these characteristics and how we will optimize their representations.