Detection of coronal mass ejections using AdaBoost on grayscale statistic features

We present an automatic algorithm to detect coronal mass ejections (CMEs) in Large Angle Spectrometric Coronagraph (LASCO) C2 running difference images. The algorithm includes 3 steps: (1) split the running difference images into blocks according to slice size and analyze the grayscale statistics of the blocks from a set of images with and without CMEs; (2) select the optimal parameters for slice size, gray threshold and fraction of the bright points and (3) use AdaBoost to combine the weak classifiers designed according to the optimal parameters. Experimental results show that our method is effective and has a high accuracy rate.