Supervised color constancy for machine vision

摘要

Abstract: In machine vision, color constancy is the ability to match object colors in images taken under different colors of illumination. This is a difficult problem because the image color will depend upon the spectral reflectance function of the object and the spectral distribution function of the incident light, both of which are generally unknown. Previous methods to solve this problem have represented these functions with a small number of basis functions and used some sort of reference knowledge to calculate the coefficients. Most of these methods have the weakness that the reference property may not actually hold for all images, or that it provides too few constraints to allow an adequate recovery of the functions. We present here a method for color constancy that uses a color chart of known spectral characteristics to give stronger reference criteria, and with a large number of colors to give enough constraints to calculate the illuminant to the desired degree of accuracy. We call this approach 'supervised color constancy' since the process is supervised by a picture of a reference color chart. We demonstrate two methods for computing supervised color constancy, one using least squares estimation, the other using a neural network. We present results for simulated experiment of the calculation of the spectral power distribution of an unknown illuminant.!