Polarization-Based Material Classification from Specular Reflection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Separation of Reflection Components Using Color and Polarization
International Journal of Computer Vision
Digital Image Processing
Diffuse-Specular Separation and Depth Recovery from Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
IEEE Transactions on Pattern Analysis and Machine Intelligence
Specular Reflection Reduction with Multi-Flash Imaging
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Separating Reflection Components of Textured Surfaces Using a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Lambert: Reconstructing Specular Surfaces Using Color
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Mesostructure from Specularity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Chromaticity-based separation of reflection components in a single image
Pattern Recognition
Physically Based Rendering, Second Edition: From Theory To Implementation
Physically Based Rendering, Second Edition: From Theory To Implementation
Real-time specular highlight removal using bilateral filtering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Specularity removal in images and videos: a PDE approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Region growing: a new approach
IEEE Transactions on Image Processing
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In this paper, we propose a new reflectance separation model to separate the diffuse and specular reflection components. The model is based on a two-dimensional space called Ch-CV space, which is spanned by maximum chromaticity (Ch) and the coefficient of variation (CV) of RGB color. The space exhibits a more direct correspondence to diffuse and specular reflection components than the RGB color space, as well as the HSI color space. Under the whitened illumination, the surface points with the same diffuse chromaticity have the same slope in Ch-CV space. Based on these properties, we propose a slope-based region growing method to implement an image segmentation in the specular regions, and to separate the reflection components for each segmented region. The comparison experiments with several state-of-the-art algorithms show its superior capability to separate the specular and diffuse reflection components.