Modeling Light Reflection for Computer Color Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of specularity using colour and multiple views
Image and Vision Computing - Special issue: 2nd European Conference on Computer Vision
International Journal of Computer Vision
Separation of Reflection Components Using Color and Polarization
International Journal of Computer Vision
Solving for Colour Constancy using a Constrained Dichromatic Reflection Model
International Journal of Computer Vision
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffuse-Specular Separation and Depth Recovery from Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Illumination chromaticity estimation using inverse-intensity chromaticity space
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Separating Reflection Components of Textured Surfaces Using a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Chromaticity-based separation of reflection components in a single image
Pattern Recognition
Evolutive Parametric Approach for Specular Correction in the Dichromatic Reflection Model
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Correction of color information of a 3D model using a range intensity image
Computer Vision and Image Understanding
Bayesian Reflectance Component Separation
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
A Solution of the Dichromatic Model for Multispectral Photometric Invariance
International Journal of Computer Vision
Estimation of multiple illuminants based on specular highlight detection
CCIW'11 Proceedings of the Third international conference on Computational color imaging
A geometrical method of diffuse and specular image components separation
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A new photographing apparatus for skin maps of human face rendering
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Specularity removal in images and videos: a PDE approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Specular removal using CL-projection
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
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
User-assisted image compositing for photographic lighting
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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Many algorithms in computer vision assume diffuse only reflections and deem specular reflections to be outliers. However, in the real world, the presence of specular reflections is inevitable since there are many dielectric inhomogeneous objects which have both diffuse and specular reflections. To resolve this problem, we present a method to separate the two reflection components. The method is principally based on the distribution of specular and diffuse points in a two-dimensional maximum chromaticity-intensity space. We found that, by utilizing the space and known illumination color, the problem of reflection component separation can be simplified into the problem of identifying diffuse maximum chromaticity. To be able to identify the diffuse maximum chromaticity correctly, an analysis of the noise is required since most real images suffer from it. Unlike existing methods, the proposed method can separate the reflection components robustly for any kind of surface roughness and light direction.