Color Transparency


Peggy Gerardin


The visual system identifies surfaces and uses their properties to help recognize objects. One example of this phenomenon is color transparency: when a surface is seen both in plain view and through a transparent overlay, the visual system still identifies it as a single surface. Several studies have suggested that color changes across a region of an image that can be described as translations and/or convergences in a linear trichromatic color space lead to the perception of transparency, but other transformations, such as shear and rotation, do not. To study the limits of such systemic chromatic changes, we generated classes of stimuli consistent and inconsistent with D'Zmura et al's Convergence Model of transparency perception. The main results support the Convergence Model in showing that, for vectors exceeding a minimal length, convergence and translation (except when equiluminant) lead to the perception of transparency, while shear and divergence do not. Large equiluminant translations were less often judged as transparent, consistent with observations reported by Chen and D'Zmura with respect to color changes that cross hue boundaries. Surprisingly, we found that small shears and divergences were also classified as transparent, in contradiction with the model. This could imply that two mechanisms underlie the perception of transparency: a low contrast mechanism that is sensitive to chromatic and luminance change independent of its direction (translation, shear, convergence or divergence), and one sensitive to higher contrasts that depends on the integrated direction. Our goal is to establish a model of color transparency, defining optimal conditions where this phenomenon occurs, and to investigate its application to color image rendering algorithms.

Related work

Transparency and motion
Transparency and shadow

Main contributions

This research will be useful to solve problems in color gamut transfers between devices (gamut mapping) and image segmentation. Image segmentation is the first step in image analysis and pattern recognition. The actual methods are based on histogram thesholding, region or edge-based approaches, and then image-independant. With our research, we will try to find an image-dependant color segmentation method that allows separating illuminance from surface colors, and mapping individual image components separately.

Some results...

Current and future research

Following is a short summary of the next steps:


Sabine Süsstrunk
Ken Knoblauch (INSERM, France)



Swiss National Science Foundation (SNF) under grant number 20-59038.99.