Name
Abstract | ||
|---|---|---|
Artificial lights, which are powered by alternating current (AC), are ubiquitous nowadays. The intensity of these lights fluctuates dynamically depending on the AC power. In contrast to previous color constancy methods that exploited the spatial color information, we propose a novel deep learning-based color constancy method that exploits the temporal variations exhibited by AC-powered lights. Using a high-speed camera, we capture the intensity variations of AC lights. Then, we use these variations as an important cue for illuminant learning. We propose a network composed of spatial and temporal branches to train the model with both spatial and temporal features. The spatial branch learns the conventional spatial features from a single image, whereas the temporal branch learns the temporal features of AC-induced light intensity variations in a high-speed video. The proposed method calculates the temporal correlation between the high-speed frames to extract the effective temporal features. The calculations are done at a low computational cost and the output is fed into the temporal branch to help the model concentrate on illuminant-attentive regions. By learning both spatial and temporal features, the proposed method performs remarkably under a complex illuminant environment in a real world scenario in which color constancy is difficult to investigate. The experimental results demonstrate that the proposed method produces lower angular error than the previous state-of-the-art by 30%, and works exceptionally well under various illuminants, including complex ambient light environments. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3147252 | IEEE ACCESS |
Keywords | DocType | Volume |
Image color analysis, Estimation, Reflection, Feature extraction, Deep learning, Correlation, Convolutional neural networks, Temporal color constancy, temporal correlation, AC light, high-speed video | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun-Sang Yoo | 1 | 3 | 2.11 |
| Kang-Kyu Lee | 2 | 0 | 0.34 |
| Chan-Ho Lee | 3 | 0 | 0.34 |
| Ji-Min Seo | 4 | 0 | 0.34 |
| Jong-Ok Kim | 5 | 1 | 3.07 |