Automatic detection of prominence eruption using consecutive solar images

Prominences are clouds of relatively cool and dense gas in the solar atmosphere. In this paper, we present a new method to detect and characterize the prominence eruptions. The input is a sequence of consecutive H α solar images, and the output is a list of prominence eruption events detected. We extract the limb events and measure their associated properties by applying image processing techniques. First, we perform image normalization and noise removal. Then, we isolate the limb objects and identify the prominence features. Finally, we apply pattern recognition techniques to classify the eruptive prominences. The characteristics of prominence eruptions, such as brightness, angular width, radial height and velocity are measured. The method presented can lead to automatic monitoring and characterization of solar events.