Multi-Illuminant Estimation With Conditional Random Fields
Multi-Illuminant Estimation With Conditional Random Fields
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DOI：
10.1109/tip.2013.2286327
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发表时间：
2014-01-01
影响因子：
10.6
通讯作者：
Angelopoulou, Elli
中科院分区：
文献类型：
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作者：
Beigpour, Shida;Riess, Christian;Angelopoulou, Elli
大多数现有的颜色恒常性算法假设均匀照明。然而，在现实世界的场景中，情况往往并非如此。因此，我们提出了一个新的框架，用于估计多个光源的颜色和它们在场景中的空间分布。我们制定这个问题作为一个能量最小化的任务内的条件随机场在一组本地照明估计。为了定量评估所提出的方法，我们创建了一个新的数据集的两个主导光源的图像，包括实验室，室内和室外场景。与以前的工作不同，我们的数据库包括准确的像素明智的地面实况照明信息。我们的方法的性能进行评估多个数据集。实验结果表明，我们的框架明显优于单光源估计，以及最近提出的多光源估计方法。
Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.