Automated Coronal-Loop Detection based on Contour Extraction and Contour Classification from the SOHO/EIT Images

Details Automated Coronal-Loop Detection based on Contour Extraction and Contour Classification from the SOHO/EIT Images Automated Coronal-Loop Detection based on Contour Extraction and Contour Classification from the SOHO/EIT Images

Jul 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010soph..264..383d&link_type=abstract

Solar Physics, Volume 264, Issue 2, pp.383-402

Physics

1

Coronal Loop, Curve Tracing, Automatic Loop Detection, Classification, Image Retrieval

Scientific paper

Arch-shaped coronal loops that are isolated from the background are typically acquired manually from massive online image databases to be used in solar coronal research. The manual search for special coronal loops is not only subject to human mistakes but is also time consuming and tedious. In this study, we propose a completely automated image-retrieval system that identifies coronal-loop regions located outside of the solar disk from 17.1 nm EIT images. To achieve this aim, we first apply image-preprocessing techniques to bring out loop structures from their background and to reduce the effect of undesired patterns. Then we extract principal contours from the solar image regions. The geometrical attributes of the extracted principal contours reveal the existence of loops in a given region. Our completely automated decision-making procedure gives promising results in separating the regions with loops from the regions without loops. Based on our loop-detection procedure, we have developed an automated image-retrieval tool that is capable of retrieving images containing loops from a collection of solar images.

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