Name
Abstract | ||
|---|---|---|
Color constancy is an important task in computer vision. By analyzing the image formation model, color gamut data under one light source can be mapped to a hyperplane whose normal vector is only determined by its light source. Thus, the canonical light source is represented through the kernel method, which trains the color data. When an image is captured under an unknown illuminant, the image-corrected matrix is obtained through optimization. After being mapped to the high-dimensional space, the corrected color data are best fit for the hyperplane of the canonical illuminant. The proposed unsupervised feature-mining kernel method only depends on the color data without any other information. The experiments on the standard test datasets show that the proposed method achieves comparable performance with other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005698104310436 | ICPRAM |
Field | DocType | Citations |
Color constancy,Computer vision,Color space,Pattern recognition,Color histogram,Computer science,Color balance,Color depth,Color model,Artificial intelligence,Color normalization,ICC profile | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Xu | 1 | 14 | 4.37 |
| Huaxin Xiao | 2 | 22 | 8.41 |
| Yu Liu | 3 | 7 | 4.48 |
| Maojun Zhang | 4 | 314 | 48.74 |