Abstract
Auroral oval segmentation is of great significance to the study of auroral activities. In this paper, we propose an automatic maximal similarity based region merging (MSRM) method with a feedback based on shape information. Firstly, K-means method is employed to mark auroral oval points and background points, thus guiding the process of MSRM to obtain the initial segmentation result. Then the direct least-square ellipse fitting method is used to fit an ellipse on the initial boundary and points in the fitted ellipse are set as adjusted markers of auroral oval. Finally, the MSRM mechanism is used again to get the final segmentation result. Experimental results show that the proposed algorithm obtains a good performance.
s
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Germany, G.A., Parks, G.K., Ranganath, H., Elsen, R., Richards, P.G., Swift, W., Spann, J.F., Brittnacher, M.: Analysis of auroral morphology: Substorm precursor and onset on January 10, 1997. Geophysical Research Letters 25(15), 3043–3046 (1998)
Li, X., Ramachandran, R., He, M., Movva, S., Rushing, J., Graves, S., Lyatsky, W., Tan, A., Germany, G.: Comparing different thresholding algorithms for segmenting auroras. In: Proceedings of the International Conference on Information Technology: Coding and Computing, Las Vegas, vol. 2, pp. 594–601 (2004)
Hung, C.C., Germany, G.: K-means and iterative selection algorithms in image segmentation. IEEE Southeastcon (Session 1: Software Development) (2003)
Cao, C.-G., Newman, T.S., Germany, G.A.: New shape-based auroral oval segmentation driven by LLS-RHT. Pattern Recognitio 42(5), 607–618 (2009)
Ning, J.-F., Zhang, L., Zhang, D., Wu, C.-K.: Interactive image segmentation by maximal similarity based region merging. Pattern Recognition 43(2), 445–456 (2010)
Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 476–480 (1999)
Hones, E.W., Craven, J.D., Frank, L.A., Evans, D.S., Newell, P.T.: The horse-collar aurora: A frequent pattern of the aurora in quiet times. Geophysical Research Letter 16(1), 37–40 (1989)
Wang, Y., Hu, B.-G.: Derivations of Normalized Mutual Information in Binary Classifications. In: Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, pp. 155–163 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Gao, X., Han, B., Yang, X. (2013). An Automatic MSRM Method with a Feedback Based on Shape Information for Auroral Oval Segmentation. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_94
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)