Journal
JOURNAL OF VISION
Volume 6, Issue 11, Pages 1267-1281Publisher
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/6.11.10
Keywords
color constancy; psychophysical data; illuminants; Bayesian algorithm; computational neuroscience
Categories
Funding
- NATIONAL EYE INSTITUTE [R01EY010016, R29EY010016] Funding Source: NIH RePORTER
- NEI NIH HHS [R01 EY010016, R01 EY010016-13, EY 10016] Funding Source: Medline
Ask authors/readers for more resources
Vision is difficult because images are ambiguous about the structure of the world. For object color, the ambiguity arises because the same object reflects a different spectrum to the eye under different illuminations. Human vision typically does a good job of resolving this ambiguity-an ability known as color constancy. The past 20 years have seen an explosion of work on color constancy, with advances in both experimental methods and computational algorithms. Here, we connect these two lines of research by developing a quantitative model of human color constancy. The model includes an explicit link between psychophysical data and illuminant estimates obtained via a Bayesian algorithm. The model is fit to the data through a parameterization of the prior distribution of illuminant spectral properties. The fit to the data is good, and the derived prior provides a succinct description of human performance.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available