Comparison of Three Segmentation Techniques for Auroral Arc Detection
Author: 
Qiuju YangAuthors Info & Claims
Pages 152 - 155
Abstract
Auroral arc is the brightest and most obvious kind of aurora. Detecting auroral arc contour from all-sky imagery (ASI) images is not a trivial problem. The location, shape, and intensity of the arc vary depending on the factors such as the date, time of the day, etc; in addition, the intensity of both the auroral arc itself and the background area differs a lot in an image, which make the segmentation of auroral arc difficult. In this study, three image segmenting algorithms, including adaptive fuzzy thresholding, integrating spatial fuzzy clustering with level set methods, and region growing are applied to a selected set of ASI arc images to examine their effectiveness in auroral arc boundary detection. The results from the three techniques are presented, and the merits and demerits of each method are discussed.
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Index Terms
- Comparison of Three Segmentation Techniques for Auroral Arc Detection
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Published In
August 2016
360 pages
ISBN:9781450348508
DOI:10.1145/3007669
Copyright © 2016 ACM.
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- Xidian University
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Published: 19 August 2016
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ICIMCS'16
ICIMCS'16: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2016
Xi'an, China
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ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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