Detection and Tracking of Coronal Mass Ejections

Authors:
Goussies Norberto;Mejail Marta;Jacobo Julio;Stenborg Guillermo
Affiliations:
Computer Science Department, School of Exact and Natural Sciences, University of Buenos Aires,;Computer Science Department, School of Exact and Natural Sciences, University of Buenos Aires,;Computer Science Department, School of Exact and Natural Sciences, University of Buenos Aires,;Centre for Solar Physics and Space Weather, Catholic University of America,
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

Citing 5
Cited 0

Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Real-Time Tracking Using Level Sets

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A nonparametric statistical method for image segmentation using information theory and curve evolution

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Coronal Mass Ejection (CME) events refer to the appearance of a new, discrete, white-light feature (with outward velocity) in a coronagraph. The huge amount of data provided by the pertinent instruments onboard the Solar and Heliospheric Observatory (SOHO) and, most recently, the Solar Terrestrial Relations Observatory (STEREO) makes the human-based detection of such events excessively time consuming. Although several algorithms have been proposed to address this issue, there is still lack of universal consensus about their reliability. This work presents a novel method for the detection and tracking of CMEs as recorded by the LASCO instruments onboard SOHO. The algorithm we developed is based on level sets and region competition methods, the CMEs texture being characterized by their co-ocurrence matrix. The texture information is introduced in the region competition motion equations, and in order to evolve the curve, a fast level set implementation is used.