Modeling Light Reflection for Computer Color Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color constancy from mutual reflection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color by Correlation: A Simple, Unifying Framework for Color Constancy
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving for Colour Constancy using a Constrained Dichromatic Reflection Model
International Journal of Computer Vision
Recovering the Shading Image under Known Illumination
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Separating Reflection Components Based on Chromaticity and Noise Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutive Parametric Approach for Specular Correction in the Dichromatic Reflection Model
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Enhancement and Registration Schemes for Matching Conjunctival Vasculature
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Real-time specular highlight removal using bilateral filtering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Linked multi-component mobile robots: Modeling, simulation and control
Robotics and Autonomous Systems
Highlight detection and removal based on chromaticity
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Hybrid color space transformation to visualize color constancy
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Surface reflectance and normal estimation from photometric stereo
Computer Vision and Image Understanding
Specular highlight removal using reflection component separation and joint bilateral filtering
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Existing color constancy methods cannot handle both uniform colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors, and become error prone when there are only few surface colors. In contrast, dichromaticbased methods can successfully handle uniformly colored surfaces, but cannot be applied to highly textured surfaces since they require precise color segmentation. In this paper, we present a single integrated method to estimate illumination chromaticity from single/multi-colored surfaces. Unlike the existing dichromatic-based methods, the proposed method requires only rough highlight regions, without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in "inverse-intensity chromaticity space", a new two-dimensional space we introduce. In addition, by utilizing the Hough transform and histogram analysis in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface. Experimental results on real images show the effectiveness of the method.