Abstract
Reliable specularity detection can affect the accuracy of further image analysis. The majority of specularity detection algorithms are based on the chromaticity of the regions of specular highlights. They assume that the color of specular highlights of dielectrics is approximately the color of the incident light. We will show how this assumption is inaccurate especially in multispectral images. We propose a new, physics-based specularity detection method, which depends on the Fresnel term of the specular highlight, instead of assumptions on chromaticity space. We compute at each pixel an approximation to the Fresnel term at various wave-lengths. We then use mean-shift analysis to segment the image based on the Fresnel information. Our experiments with multispectral, as well as traditional RGB images, show improved specularity detection and higher robustness in chromaticity space noise.