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Color Constancy Using KL-Divergence (2001)
| Content Provider | CiteSeerX |
|---|---|
| Author | Hebert, Martial Thrun, Sebastian Rosenberg, Charles |
| Description | In In IEEE International Conference on Computer Vision |
| Abstract | Color is a useful feature for machine vision tasks. However, its effectiveness is often limited by the fact that the measured pixel values in a scene are influenced by both object surface reflectance properties and incident illumination. Color constancy algorithms attempt to compute color features which are invariant of the incident illumination by estimating the parameters of the global scene illumination and factoring out its effect. A number of recently developed algorithms utilize statistical methods to estimate the maximum likelihood values of the illumination parameters. This paper details the use of KL-divergence as a means of selecting estimated illumination parameter values. We provide experimental results demonstrating the usefulness of the KL-divergence technique for accurately estimating the global illumination parameters of real world images. 1 |
| File Format | |
| Publisher Date | 2001-01-01 |
| Access Restriction | Open |
| Subject Keyword | Illumination Parameter Value Color Feature Illumination Parameter Useful Feature Paper Detail Machine Vision Task Color Constancy Algorithm Attempt Pixel Value Global Scene Illumination Algorithm Utilize Statistical Method Real World Image Kl-divergence Technique Color Constancy Using Kl-divergence Maximum Likelihood Value Experimental Result Incident Illumination Object Surface Reflectance Property Global Illumination Parameter |
| Content Type | Text |
| Resource Type | Conference Proceedings |
