  The repeatability and efficiency of a corner detector determines how likely
it is to be useful in a real-world application. The repeatability is importand
because the same scene viewed from different positions should yield features
which correspond to the same real-world 3D locations [Schmid et al 2000]. The
efficiency is important because this determines whether the detector combined
with further processing can operate at frame rate.
  Three advances are described in this paper. First, we present a new heuristic
for feature detection, and using machine learning we derive a feature detector
from this which can fully process live PAL video using less than 5% of the
available processing time. By comparison, most other detectors cannot even
operate at frame rate (Harris detector 115%, SIFT 195%). Second, we generalize
the detector, allowing it to be optimized for repeatability, with little loss
of efficiency. Third, we carry out a rigorous comparison of corner detectors
based on the above repeatability criterion applied to 3D scenes. We show that
despite being principally constructed for speed, on these stringent tests, our
heuristic detector significantly outperforms existing feature detectors.
Finally, the comparison demonstrates that using machine learning produces
significant improvements in repeatability, yielding a detector that is both
very fast and very high quality.
