Automated Principal Curve Detection in Images of Solar Coronal Loops Conclusions There are many difficult problems that arise in finding only the most prominent loop shapes in an image. Many times the shapes present themselves as many segments and the algorithm must fill in the gaps between them, while correctly not filling in the gaps going to segments that are part of other shapes. Determining which shape a segment belongs to of course requires identifying key characteristics of each segment, such as their location, orientation, and curvature direction and degree. The algorithm used here seems promising in its ability to identify only the most interesting loop shapes. It will hopefully be useful in solving this problem for astrophysicists who analyze solar images, and others who could benefit from detection of loop- shaped curves. -Input Image (after ridge detection) -Ideal Output Image (manually traced) (Gaps, noise, non-loop segments) (Gaps closed, noise cleaned, principal segments only) Motivation •We aim to automate the principal loop finding process by automatically tracing curves in an image, determining which segments represent separate curve shapes, closing gaps within curves, and lastly keeping only the longest, most interesting, smoothest curve segments. Garett Ridge, Nurcan Durak, Dr. Olfa Nasraoui Knowledge Discovery and Web Mining Lab Department of Computer Engineering and Computer Science, University of Louisville {g0ridg01, nurcan.durak, olfa.nasraoui}@louisville.edu References [1] B. Inhester, L. Feng and T. Wiegelmann: "Segementation of Loops from Coronal EUV Images", Solar Physics, Volume 248, Number 2/April, 2008 [2] Nurcan Durak, Olfa Nasraoui: "Feature Exploration for Mining Coronal Loops from Solar Images". ICTAI (1) 2008: 547-550 [3] NASA EIT Catalog : http://umbra.nascom.nasa.gov/eit/eit-catalog.html [4] Jack E. Bresenham, "Algorithm for computer control of a digital plotter", IBM Systems Journal, Vol. 4, No.1, January 1965, pp. 25-30 SOFTWARE USED - Matlab 2003 Problem • NASA has thousands of solar images in a database [3] that has been growing since 1996. • Astrophysicists currently waste a lot of time manually doing the following: • Downloading images from NASA solar image databases • Looking at every solar image to detect which ones have loops • Determining the exact position of coronal loops. • This problem is made complex by missing parts (gaps) in the curve segments, by erroneous junctions between unrelated segments, by jaggedness in segments, and by other forms of noise and clutter. Only the principal coronal loops are desired from this cluttered image.

Automated Principal Curve Detection in Images of Solar Coronal Loops

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Automated Principal Curve Detection in Images of Solar Coronal Loops. Garett Ridge, Nurcan Durak, Dr. Olfa Nasraoui Knowledge Discovery and Web Mining Lab Department of Computer Engineering and Computer Science, University of Louisville {g0ridg01, nurcan.durak, olfa.nasraoui}@louisville.edu. - PowerPoint PPT Presentation

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Automated Principal Curve Detection in Images of Solar Coronal Loops

ConclusionsThere are many difficult problems that arise in finding only the most prominent loop shapes

in an image. Many times the shapes present themselves as many segments and the algorithm must fill in the gaps between them, while correctly not filling in the gaps going to segments that are part of other shapes. Determining which shape a segment belongs to of course requires identifying key characteristics of each segment, such as their location, orientation, and curvature direction and degree.

The algorithm used here seems promising in its ability to identify only the most interesting loop shapes. It will hopefully be useful in solving this problem for astrophysicists who analyze solar images, and others who could benefit from detection of loop-shaped curves.

-Input Image (after ridge detection) -Ideal Output Image (manually traced)

(Gaps, noise, non-loop segments) (Gaps closed, noise cleaned, principal segments only)

Motivation•We aim to automate the principal loop finding process by automatically tracing curves in an image, determining which segments represent separate curve shapes, closing gaps within curves, and lastly keeping only the longest, most interesting, smoothest curve segments.

Garett Ridge, Nurcan Durak, Dr. Olfa Nasraoui

Knowledge Discovery and Web Mining LabDepartment of Computer Engineering and Computer Science, University of Louisville

{g0ridg01, nurcan.durak, olfa.nasraoui}@louisville.edu

References[1] B. Inhester, L. Feng and T. Wiegelmann: "Segementation of Loops from Coronal EUV Images", Solar Physics, Volume 248, Number 2/April, 2008[2] Nurcan Durak, Olfa Nasraoui: "Feature Exploration for Mining Coronal Loops from Solar Images". ICTAI (1) 2008: 547-550[3] NASA EIT Catalog : http://umbra.nascom.nasa.gov/eit/eit-catalog.html[4] Jack E. Bresenham, "Algorithm for computer control of a digital plotter", IBM Systems Journal, Vol. 4, No.1, January 1965, pp. 25-30 SOFTWARE USED - Matlab 2003

Problem• NASA has thousands of solar images in a database [3] that has been growing since

1996.

• Astrophysicists currently waste a lot of time manually doing the following: • Downloading images from NASA solar image databases• Looking at every solar image to detect which ones have loops• Determining the exact position of coronal loops.

• This problem is made complex by missing parts (gaps) in the curve segments, by erroneous junctions between unrelated segments, by jaggedness in segments, and by other forms of noise and clutter. Only the principal coronal loops are desired from this cluttered image.