  The solar surface and atmosphere are highly dynamic plasma environments,
which evolve over a wide range of temporal and spatial scales. Large-scale
eruptions, such as coronal mass ejections, can be accelerated to millions of
kilometres per hour in a matter of minutes, making their automated detection
and characterisation challenging. Additionally, there are numerous faint solar
features, such as coronal holes and coronal dimmings, which are important for
space weather monitoring and forecasting, but their low intensity and sometimes
transient nature makes them problematic to detect using traditional image
processing techniques. These difficulties are compounded by advances in ground-
and space- based instrumentation, which have increased the volume of data that
solar physicists are confronted with on a minute-by-minute basis; NASA's Solar
Dynamics Observatory for example is returning many thousands of images per hour
(~1.5 TB/day). This chapter reviews recent advances in the application of
images processing techniques to the automated detection of active regions,
coronal holes, filaments, CMEs, and coronal dimmings for the purposes of space
weather monitoring and prediction.
