Ensemble of illumination estimation methods using support vector regression
Ensemble of illumination estimation methods using support vector regression
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DOI：
10.1117/12.2590968
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Youngha Chang;Takuya Iiyama;N. Mukai
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文献类型：
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作者：
Youngha Chang;Takuya Iiyama;N. Mukai
光照估计是许多计算机视觉应用的基本前提。在本文中，我们结合联合收割机的一些以前的方法更有效的估计。SVR用于集成以前的方法。我们使用高光谱图像作为数据集，而不是使用标准的RGB图像数据集，因为我们可以自由地使用它们设置各种光源颜色，并且可以获得准确的地面真值。为了渲染高光谱图像，我们准备了具有21种不同色温的光谱分布，并使用普朗克黑体辐射方程以500 [K]的间隔产生色温范围从2，000 [K]到12，000 [K]的光源光谱。用于训练的高光谱图像的数量是16。每个高光谱图像包含每个像素33个反射数据。本文结合的光照估计方法共有6种方法，5种传统的光照估计方法，以及一种基于深度学习的方法。我们比较了传统的光照估计方法与所提出的方法，并得出结论，所提出的方法可以实现更高的预测精度。
Illumination estimation is a fundamental prerequisite for many computer vision applications. In this paper, we combine some previous methods for more effective estimation. SVR was used for the ensemble of previous methods. Instead of using the standard RGB image dataset, we have used hyperspectral images as the dataset, because we can freely set varieties of illuminant colors with them and can get accurate ground truth values. To render the hyperspectral image, we prepare spectral distribution with 21 different color temperatures and generate illuminant spectrums using Planck blackbody radiation equation with color temperature ranging from 2,000 [K] to 12,000 [K] at 500 [K] intervals. The number of hyperspectral images used for the training is 16. Each hyperspectral image contains 33 reflection data per each pixel. Illumination estimation methods combined in this paper are 6 methods in total, five traditional illuminant estimation methods, and one deep learning-based approach. We have compared the conventional illumination estimation methods with the proposed methods, and have concluded that the proposed method can achieve higher prediction accuracy.