Color Constancy for RGB and Multispectral Images

The problem of inferring the light color for a scene is called Illuminant Estimation. This step forms the first task in many workflows in the larger task of discounting the effect of the color of the illuminant, which is called Color Constancy. Illuminant Estimation is typically used as a pre-processing step in many computer vision tasks. In this thesis, we tackle this problem for both RGB and multispectral images. First, for RGB images we extend a moments based method in several ways: firstly by replacing the standard expectation value, the mean, considering moments that are based on a Minkowski p-norm; and then secondly by going over to a float value for the parameter p and carrying out a nonlinear optimization on this parameter; and finally by considering a different expectation value, generated by using the geometric mean. We show that these strategies can drive down the median and maximum error of illuminant estimates. And then for multispectral images, we formulate a multiple-illuminants estimation problem as a Conditional Random Field (CRF) optimization task over local estimations. We then improve local illuminant estimation by incorporating spatial information in each local patch.