Estimating Illumination Chromaticity via Support Vector Regression

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Faculty/Staff
Final version published as: 

Funt, B., and Xiong, W., "Estimating Illumination Chromaticity via Support Vector Regression," Proc.  Twelfth IS&T/SID Color Imaging Conference: Color Science, Systems & Applications, Scottsdale, AZ. Nov. 2004.

Date created: 
2004-11
Abstract: 

The technique of support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the scene. Illumination estimation is fundamental to white balancing digital color images and to understanding human color constancy. Under controlled experimental conditions, the support vector method is shown to perform better than the neural network and color by correlation methods.

Description: 

Presented at the CIC 2004 IS&T/SID Color Imaging Conference, Nov. 2004.

Language: 
English
Document type: 
Conference presentation
Rights: 
Rights remain with the authors.
File(s): 
Sponsor(s): 
Natural Sciences and Engineering Research Council of Canada (NSERC)
Statistics: