Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separation of Reflection Components Using Color and Polarization
International Journal of Computer Vision
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Separating Reflection Components Based on Chromaticity and Noise Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination chromaticity estimation using inverse-intensity chromaticity space
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Estimation of multiple illuminants based on specular highlight detection
CCIW'11 Proceedings of the Third international conference on Computational color imaging
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The presence of highlight can lead to erroneous results in Computer Vision applications such as edge detection, and motion tracking. Many algorithms have been developed to detect and remove highlight. In this paper, we propose a simple and effective method for detecting and removal of highlight. We first use a window to help to remove the noise and reduce the data amount for analysis. We then apply K-means algorithm in a 5-D vector space to computer diffuse chromaticity. In the case of non-white illuminant, illuminant chromaticity is estimated in the inverse-intensity space, and we use Fuzzy C-mean clustering and linear fitting to get illuminant chromaticity. Finally, we use Specular-to-Diffuse mechanism to separate specular reflection component from image. Experiments show that it is robust and can give good results.