Illuminant Estimation by Voting

Obtaining an estimate of the illuminant color is an important component in many image analysis applications. Due to the complexity of the problem many restrictive assumptions are commonly applied, making the existing illuminant estimation methodologies not widely applicable on natural images. We propose a methodology which analyzes a large number of regions in an image. An illuminant estimate is obtained independently from each region and a global illumination color is computed by consensus. Each region itself is mainly composed by pixels which simultaneously exhibit both diffuse and specular reflection. This allows for a larger inclusion of pixels than purely specularitybased methods, while avoiding, at the same time, some of the restrictive assumptions of purely diffuse-based approaches. As such, our technique is particularly well-suited for analyzing real-world images. Experiments with laboratory data show that our methodology outperforms 75 % of other illuminant estimation methods. On natural images, the algorithm is very stable and provides qualitatively correct estimates. 1

E.: The narrow-band assumption in logchromaticity space

Abstract. Despite the strengths and popularity of the log-chromaticity space (LCS), there is still a significant amount of concern regarding its narrow-band assumption (NBA). Though not always necessary, this assumption is relatively common, as it leads to elegant formulations. We present a scheme for evaluating whether a deviation from the NBA will have an impact on the expected LCS values. We also introduce two metrics for measuring the divergence from the expected behavior under the NBA in LCS. Lastly, we empirically analyze how different types of reflectance spectra are affected in varying degrees by this assumption. For example, experiments with real and synthetic data show that the violation of the NBA typically has insignificant impact on bright unsaturated colors.

0�1��22��(��������&����34��55−−∀�22���6 7��8��������∗�9�������#�5∀−!�∀2#!! � BEYOND THE NEUTRAL INTERFACE REFLECTION ASSUMPTION IN ILLUMINANT COLOR ESTIMATION

The neutral interface reflection (NIR) assumption is a widely accepted theory in computer vision. According to the NIR the color of specularities of dielectric materials is the color of the incident illumination and the influence of the Fresnel reflectance is neglected. We show, that there is a material- and geometry-dependent shift between the color of the specularity and the color of the incident light due to the Fresnel effect which for human skin can be up to approximately 5.8%. As the NIR concept is often the core idea of specularity-based illuminant-color estimation techniques, the ignored Fresnel effect introduces a systematic error in the estimation result. We thus propose a material-dependent rectification method for correcting this color shift. Our experiments on human skin regions show an average improvement of the illuminant color estimation of about 30%. Index Terms — Neutral interface reflection, Fresnel reflectance, Cook-Torrance reflection model, illumination estimation