  A new algorithm for automatic detection of prominences on the solar limb in
304 A EUV images is presented, and results of its application to SOHO/EIT data
discussed. The detection is based on the method of moments combined with a
classifier analysis aimed at discriminating between limb prominences, active
regions, and the quiet corona. This classifier analysis is based on a Support
Vector Machine (SVM). Using a set of 12 moments of the radial intensity
profiles, the algorithm performs well in discriminating between the above three
categories of limb structures, with a misclassification rate of 7%. Pixels
detected as belonging to a prominence are then used as starting point to
reconstruct the whole prominence by morphological image processing techniques.
It is planned that a catalogue of limb prominences identified in SOHO and
STEREO data using this method will be made publicly available to the scientific
community.
