Name
Abstract | ||
|---|---|---|
Computational color constancy aims to estimate the color of the light source. The performance of many vision tasks, such as object detection and scene understanding, may benefit from color constancy by estimating the correct object colors. Since traditional color constancy methods are based on specific assumptions, none of those methods can be used as a universal predictor. Further, shallow learning schemes are used for training-based color constancy approaches, suffering from limited learning capacity. In this paper, we propose a framework using Deep Neural Networks (DNNs) to obtain an accurate light source estimator to achieve color constancy. We formulate color constancy as a DNN-based regression approach to estimate the color of the light source. The model is trained using datasets of more than a million images. Experiments show that the proposed algorithm outperforms the state-of-the-art by 9%. Especially in cross dataset validation, reducing the median angular error by 35%. Further, in our implementation, the algorithm operates at more than $100$ fps during |
Year | Venue | Field |
|---|---|---|
2015 | BMVC | Color constancy,Object detection,Computer vision,Pattern recognition,Regression,Angular error,Computer science,Artificial intelligence,Deep learning,Color normalization,Light source,Estimator |
DocType | Citations | PageRank |
Conference | 9 | 0.48 |
References | Authors | |
22 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongyu Lou | 1 | 61 | 4.06 |
| Theo Gevers | 2 | 2973 | 214.46 |
| Ninghang Hu | 3 | 80 | 6.59 |
| Marcel P. Lucassen | 4 | 25 | 3.71 |