Ensemble of illumination estimation methods using support vector regression

Youngha Chang; Takuya Iiyama; Nobuhiko Mukai

doi:10.1117/12.2590968

13 March 2021 Ensemble of illumination estimation methods using support vector regression

Youngha Chang, Takuya Iiyama, Nobuhiko Mukai

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117660Z (2021) https://doi.org/10.1117/12.2590968
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

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.

Citation Download Citation

Youngha Chang, Takuya Iiyama, and Nobuhiko Mukai "Ensemble of illumination estimation methods using support vector regression", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117660Z (13 March 2021); https://doi.org/10.1117/12.2590968

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