Paper Info
Title
Systematic study of color spaces and components for the segmentation of sky/cloud images
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.
Year
DOI
Venue
2014
10.1109/ICIP.2014.7026033
Image Processing
Keywords
Field
DocType
clouds,fuzzy reasoning,geophysical image processing,image colour analysis,image segmentation,infrared imaging,principal component analysis,PCA,WSI-based cloud imaging,WSI-based sky imaging,Whole Sky Imagers,accurate cloud segmentation,cloud cover,cloud structure,cost-effective cloud imaging,cost-effective sky imaging,fuzzy image clustering,ground-based WSI,image color models,optimal cloud image segmentation,principal component analysis,segmentation of sky/cloud images,sky image segmentation,systematic color component selection approach,systematic color component study,systematic color space selection approach,systematic color space study,weather patterns,Clustering,Color spaces,Image segmentation,Principal Component Analysis,Remote sensing
Computer vision,Color space,Scale-space segmentation,Color histogram,Pattern recognition,Computer science,Segmentation-based object categorization,Image segmentation,Sky,Color model,Artificial intelligence,Cloud cover
Conference
Volume
ISSN
Citations 
abs/1701.04520
1522-4880
9
PageRank 
References 
Authors
1.11
2
3
Name
Order
Citations
PageRank
Soumyabrata Dev16213.94
Yee Hui Lee210724.09
Stefan Winkler321621.60