  A novel similarity-covariant feature detector that extracts points whose
neighbourhoods, when treated as a 3D intensity surface, have a saddle-like
intensity profile. The saddle condition is verified efficiently by intensity
comparisons on two concentric rings that must have exactly two dark-to-bright
and two bright-to-dark transitions satisfying certain geometric constraints.
Experiments show that the Saddle features are general, evenly spread and
appearing in high density in a range of images. The Saddle detector is among
the fastest proposed. In comparison with detector with similar speed, the
Saddle features show superior matching performance on number of challenging
datasets.
