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Automatic Detection of Limb Prominences in 304 Å EUV Images View all abstracts by submitter

Nicolas Labrosse   Submitted: 2009-12-08 03:14

A new algorithm for automatic detection of prominences on the solar limb in 304 Å EUV images is presented, and results of its application to SOHO/EIT data discussed. The detection is based on the method of moments combined with a classifier analysis aimed at discriminating between limb prominences, active regions, and the quiet corona. This classifier analysis is based on a Support Vector Machine (SVM). Using a set of 12 moments of the radial intensity profiles, the algorithm performs well in discriminating between the above three categories of limb structures, with a misclassification rate of 7%. Pixels detected as belonging to a prominence are then used as starting point to reconstruct the whole prominence by morphological image processing techniques. It is planned that a catalogue of limb prominences identified in SOHO and STEREO data using this method will be made publicly available to the scientific community.

Authors: Nicolas Labrosse, Silvia Dalla, Steve Marshall
Projects: SoHO-EIT

Publication Status: Solar Physics, accepted
Last Modified: 2009-12-08 09:52
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