z-logo
open-access-imgOpen Access
The color appearance of curved transparent objects
Author(s) -
Robert Ennis,
Katja Doerschner
Publication year - 2021
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/jov.21.5.20
Subject(s) - lightness , chromaticity , color gel , computer vision , transparency (behavior) , artificial intelligence , filter (signal processing) , color space , object (grammar) , optics , computer science , mathematics , convergence (economics) , physics , image (mathematics) , computer security , electrode , quantum mechanics , economics , economic growth , thin film transistor
Studies on colored transparent objects have elucidated potential mechanisms, but these studies have mainly focused on flat filters overlaying flat backgrounds. While they have provided valuable insight, these studies have not captured all aspects of transparency, like caustics, specular reflections/highlights, and shadows. Here, we investigate color-matching experiments with curved transparent objects for different matching stimuli: a uniform patch and a flat filter. Two instructions were tested: simply match the color of the glass object and the test element (patch and flat filter) or match the color of the dye that was used to tint the transparent object (patch). Observers’ matches differed from the mean, the most frequent, and the most saturated color of the transparent stimuli, whereas the brightest regions captured the chromaticity, but not the lightness, of patch matches. We applied four models from flat filter studies: the convergence model, the ratios of either the means (RMC) or standard deviations (RSD) of cone excitations, and a robust ratio model. The original convergence model does not fully generalize but does not perform poorly, and with modifications, we find that curved transparent objects cause a convergence of filtered colors toward a point in color space, similar to flat filters. Considering that, the RMC and robust ratio models generalized more than the RSD, with the RMC performing best across the stimuli we tested. We conclude that the RMC is probably the strongest factor for determining the color. The RSD seems instead to be related to the perceived “clarity” of glass objects.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here