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.
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© 2013 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/978-3-642-42057-3_94
Publisher Name: Springer, Berlin, Heidelberg
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