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http://dx.doi.org/10.5303/JKAS.2012.45.6.167

AUTOMATIC DETECTION AND EXTRACTION ALGORITHM OF INTER-GRANULAR BRIGHT POINTS  

Feng, Song (Computer Technology Application Key Lab of Yunnan Province and Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
Ji, Kai-Fan (Computer Technology Application Key Lab of Yunnan Province and Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
Deng, Hui (Computer Technology Application Key Lab of Yunnan Province and Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
Wang, Feng (Computer Technology Application Key Lab of Yunnan Province and Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
Fu, Xiao-Dong (Computer Technology Application Key Lab of Yunnan Province and Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
Publication Information
Journal of The Korean Astronomical Society / v.45, no.6, 2012 , pp. 167-173 More about this Journal
Abstract
Inter-granular Bright Points (igBPs) are small-scale objects in the Solar photosphere which can be seen within dark inter-granular lanes. We present a new algorithm to automatically detect and extract igBPs. Laplacian and Morphological Dilation (LMD) technique is employed by the algorithm. It involves three basic processing steps: (1) obtaining candidate "seed" regions by Laplacian; (2) determining the boundary and size of igBPs by morphological dilation; (3) discarding brighter granules by a probability criterion. For validating our algorithm, we used the observed samples of the Dutch Open Telescope (DOT), collected on April 12, 2007. They contain 180 high-resolution images, and each has a $85{\times}68\;arcsec^2$ field of view (FOV). Two important results are obtained: first, the identified rate of igBPs reaches 95% and is higher than previous results; second, the diameter distribution is $220{\pm}25km$, which is fully consistent with previously published data. We conclude that the presented algorithm can detect and extract igBPs automatically and effectively.
Keywords
techniques: image processing; Sun: granulation; Sun: photosphere;
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