  Detecting elliptical objects from an image is a central task in robot
navigation and industrial diagnosis where the detection time is always a
critical issue. Existing methods are hardly applicable to these real-time
scenarios of limited hardware resource due to the huge number of fragment
candidates (edges or arcs) for fitting ellipse equations. In this paper, we
present a fast algorithm detecting ellipses with high accuracy. The algorithm
leverage a newly developed projective invariant to significantly prune the
undesired candidates and to pick out elliptical ones. The invariant is able to
reflect the intrinsic geometry of a planar curve, giving the value of -1 on any
three collinear points and +1 for any six points on an ellipse. Thus, we apply
the pruning and picking by simply comparing these binary values. Moreover, the
calculation of the invariant only involves the determinant of a 3*3 matrix.
Extensive experiments on three challenging data sets with 650 images
demonstrate that our detector runs 20%-50% faster than the state-of-the-art
algorithms with the comparable or higher precision.
