Adaptive Technique for Merging Broken Filaments in H-α Solar Images Using Machine Learning Techniques

Ibrahim A. Atoum; Maaruf Ali

doi:10.1007/s13369-016-2328-7

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Adaptive Technique for Merging Broken Filaments in H-α Solar Images Using Machine Learning Techniques

Journal article

Peer reviewed

Adaptive Technique for Merging Broken Filaments in H-α Solar Images Using Machine Learning Techniques

Ibrahim A. Atoum and Maaruf Ali

Arabian journal for science and engineering (2011), Vol.42(2), pp.787-792

2017

DOI: https://doi.org/10.1007/s13369-016-2328-7

Abstract

Engineering

general

Humanities and Social Sciences

multidisciplinary

Research Article - Computer Engineering and Computer Science

Science

One impeding factor for determining the correct number of solar filaments in H- α 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.

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Details

Title: Adaptive Technique for Merging Broken Filaments in H-α Solar Images Using Machine Learning Techniques
Creators - without role: Ibrahim A. Atoum - Al Maaref University College
Maaruf Ali - Arden University
Publication Details: Arabian journal for science and engineering (2011), Vol.42(2), pp.787-792
Publisher: Springer Berlin Heidelberg
Identifiers: 9914077808331
Academic Unit: Almaarefa University; University Ha'il
Language: English
Resource Type: Journal article