Systematic study of color spaces and components for the segmentation of sky/cloud images
Computer Vision and Pattern Recognition
2017-01-18 v1
Abstract
Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.
Keywords
Cite
@article{arxiv.1701.04520,
title = {Systematic study of color spaces and components for the segmentation of sky/cloud images},
author = {Soumyabrata Dev and Yee Hui Lee and Stefan Winkler},
journal= {arXiv preprint arXiv:1701.04520},
year = {2017}
}
Comments
Published in Proc. IEEE International Conference on Image Processing (ICIP), Oct. 2014