Evolutive Parametric Approach for Specular Correction in the Dichromatic Reflection Model
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Bayesian Reflectance Component Separation
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Real-time specular highlight removal using bilateral filtering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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
Specularity removal in images and videos: a PDE approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Correspondence search in the presence of specular highlights using specular-free two-band images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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
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Most existing methods of reflection components decomposition using a single color image require color segmentation. Few methods that employ local operations are able to avoid the requirement; however, they usually suffer from color discontinuity problems. In this paper, we introduce a decomposition method using a single color image that does not require (global) color segmentation or (local) color discontinuity detection. The method principally utilizes the coefficients of the reflectance basis functions of input image and its specular-free image. Combining those coefficients enables us to find the diffuse coefficients of the specular pixels for every surface color. As a result, the decomposition becomes a well-posed problem and able to be solved in closed-form equations. Our experimental results on real complex textured images show the effectiveness of our proposed method.