Adaptive technique for merging broken filaments in H-Alpha solar images using machine learning techniques

Atoum, Ibrahim, A. and Ali, Maaruf (2017) Adaptive technique for merging broken filaments in H-Alpha solar images using machine learning techniques. Arabian Journal for Science and Engineering, 42 (2). pp. 787-792. ISSN 2193-567X

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Abstract

One impeding factor for determining the correct number of solar filaments in H- αα solar images is its broken appearance, which, in turn, affects the accuracy of tracking it and extracting its attributes accurately. By integrating image processing (IP) and artificial intelligence techniques, these fragmented threads have been more effectively merged. In this paper, different IP methods are utilized to extract the filament attributes, where these attributes act as inputs to a neural network to obtain a 92% true-positive rate. The method introduced in this work is fully automated and not threshold-based—unlike in previous works.

Item Type: Article
Uncontrolled Keywords: solar image, filament detection, automatic detection, filament merging, neural network
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Health & Science > Department of Science & Technology
Depositing User: David Upson-Dale
Date Deposited: 28 Nov 2017 09:02
Last Modified: 28 Nov 2017 09:08
URI: http://oars.uos.ac.uk/id/eprint/287

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