Solar data in the form of images and Carrington maps are very important resources for the study of the long-term variations of the sun. These data can help us study the solar activity features such as filaments and other prominences. Solar filaments have been long related to the Coronal Mass Ejections (CMEs). CMEs are major solar eruptions that can cause changes in the solar atmosphere and bring about geomagnetic storms on Earth. S-shaped filaments are popularly termed as Sigmoidal filaments. These structures may soon become unstable and give out large scale CMEs. Thus the identification of Sigmoidal filaments in a given solar image may help us study the relation between them and CMEs better.
Work has been done previously for the identification of filaments from solar images and Carrington Maps. This pa- per proposes a fully automated algorithm to detect sigmoidal filaments from Carrington maps without any set parameteric constants that may work in real-time. This may further play an important role in the prediction of CMEs/Flares.
