Articles | Volume 11, issue 4
https://doi.org/10.5194/amt-11-2041-2018
https://doi.org/10.5194/amt-11-2041-2018
Research article
 | 
11 Apr 2018
Research article |  | 11 Apr 2018

High-dynamic-range imaging for cloud segmentation

Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, and Stefan Winkler

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Cited articles

Akyüz, A. O.: High dynamic range imaging pipeline on the GPU, J. Real-Time Image Process., 10, 273–287, https://doi.org/10.1007/s11554-012-0270-9, 2015.
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Boykov, Y. and Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, in: Proc. International Conference on Computer Vision (ICCV), https://doi.org/10.1109/ICCV.2001.937505, 2001.
Boykov, Y. and Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision, IEEE T. Pattern. Anal., 26, 1124–1137, https://doi.org/10.1109/TPAMI.2004.60, 2004.
Boykov, Y., Veksler, O., and Zabih, R.: Fast approximate energy minimization via graph cuts, IEEE T. Pattern. Anal., 23, 1222–1239, https://doi.org/10.1109/34.969114, 2001.
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Short summary
Sky–cloud images obtained from ground-based sky cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large variation in the scene luminance. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging. We describe the entire process and also release a new database.