  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 dynamic range in terms of luminance, more than a conventional camera can
capture. It is thus difficult to capture the details of an entire scene with a
regular camera in a single shot. In most cases, the circumsolar region is
over-exposed, and the regions near the horizon are under-exposed. This renders
cloud segmentation for such images difficult. In this paper, we propose
HDRCloudSeg -- an effective method for cloud segmentation using
High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe
the HDR image generation process and release a new database to the community
for benchmarking. Our proposed approach is the first using HDR radiance maps
for cloud segmentation and achieves very good results.
