This website presents work about Color Constancy and holds the additional
material to the CVPR 2008 paper Bayesian Color
Constancy Revisited (conference poster). The main work for the paper was done during an internship of the first author
at the Vision
Group at Microsoft
Research Cambridge. You can download the dataset and the code used
for the experiments in the paper. In case of questions please write
an email to the contact person.
Color constancy is the tendency to perceive surface color
consistently, despite variations in ambient illumination. In this work
we revisted the work presented in Bayesian
Color Constancy with Non-Gaussian Models and investigated the use
of more precice reflectance priors for Color Constancy. We collected a
new dataset for Color Constancy consisting of
different scenes, both indoor and outdoor taken under different
illuminations. In each scene a Gretag MacBeth Color Checker Chart was
placed such that it was illuminated by the main scene illuminant and
thus its color could be retrieved. The code used
for the experiments in the paper is also available.
Some example scenes of the datasets are shown below. The data is
available in Canon RAW format free of any correction. For the
experiment we use the Canon Digital Photo Professional Tool to convert
the RAW images to TIFF format using the AutoWhitebalance setting of
the camera. We also corrected for chromatic
abberation in this step. The result was downsampled to 20% of the
original size. You can download the RAW images split in the archives
(about 1000MB each (!)).
Alternatively you can download the
(600MB) which comes with the extracted color of
the grey patches of the MacBeth Color Checker chart in r/g chromaticities.
In both cases you may want to download the
coordinates of the Color Checker Chart
within the images to mask
them in any experiments (file format explained in a readme file
included in the archive). An accompanying file (outdoor.txt) separates
the images in indoor and outdoor scenes.
The code is available as Matlab source code, some functions are
written as MEX files which need to be compiled first. Most functions
should have an help, so try "help < functionname >" for an
explanation. Try the function "sample_run.m" for an example run of the
Greyworld and Bayesian algorithm. We want to emphasize that the code
is research code and thus only of "prototype" quality.
For any questions about the code please write an email to
You can download the code here.
The Greyworld algorithm is based on
and almost identical in most parts with the code published by Joost van de Weijer at his color
Color Constancy Revisited - Peter V. Gehler, Carsten Rother,
Andrew Blake, Toby Sharp and Tom Minka, CVPR 2008, Bibtex Entry|
Contact person Peter Gehler