Abstract
The technique presented in this paper is based on fuzzy clustering in order to achieve robust automatic detection of active regions in solar images. The first part of the detection process is based on seed selection and region growing. After that, the regions obtained are grouped into real active regions using a fuzzy clustering algorithm. The procedure developed has been tested on 400 full-disk solar images (corresponding to 4 days) taken from the satellite SOHO. The results are compared with those manually generated for the same days and a very good correspondence is found, showing the robustness of the method described.
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Aranda, M.C., Caballero, C. (2010). Automatic Detection of Active Region on EUV Solar Images Using Fuzzy Clustering. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_8
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DOI: https://doi.org/10.1007/978-3-642-14049-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14048-8
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