SYSTEMATIC STUDY OF COLOR SPACES AND COMPONENTS FOR THE SEGMENTATION OF SKY/CLOUD IMAGES

被引：0
|
作者：
Dev, Soumyabrata [1 ]
Lee, Yee Hui [1 ]
Winkler, Stefan [2 ]
机构：
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
[2] Univ Illinois, ADSC, Singapore 138632, Singapore
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2014年
关键词：
Remote sensing; Image segmentation; Color spaces; Principal Component Analysis; Clustering; SKY IMAGES; CLOUD; CLASSIFICATION; ALGORITHM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：5102 / 5106
页数：5
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