Title:

  Bayesian Color Constancy with Non-Gaussian Models

Authors:

  Charles Rosenberg, Computer Science Department
  Thomas Minka, Statistics Department
  Alok Ladsariya, Computer Science Department
 
  Carnegie Mellon University
  Pittsburgh, PA  15213 

Abstract:

  We present a Bayesian approach to color constancy which utilizes a
  non-Gaussian probabilistic model of the image formation process.  The
  parameters of this model are estimated directly from an uncalibrated
  image set and a small number of additional algorithmic parameters are
  chosen using cross validation.  The algorithm is empirically shown to
  exhibit RMS error lower than other color constancy algorithms based on
  the Lambertian surface reflectance model when estimating the
  illuminants of a set of test images. This is demonstrated via a direct
  performance comparison utilizing a publicly available set of real
  world test images and code base.

Keywords:

  color constancy, illumination estimation