  This paper presents a new axis-based shape representation scheme along with a
matching framework to address the problem of generic shape recognition. The
main idea is to define the relative spatial arrangement of local symmetry axes
and their metric properties in a shape centered coordinate frame. The resulting
descriptions are invariant to scale, rotation, small changes in viewpoint and
articulations. Symmetry points are extracted from a surface whose level curves
roughly mimic the motion by curvature. By increasing the amount of smoothing on
the evolving curve, only those symmetry axes that correspond to the most
prominent parts of a shape are extracted. The representation does not suffer
from the common instability problems of the traditional connected skeletons. It
captures the perceptual qualities of shapes well. Therefore finding the
similarities and the differences among shapes becomes easier. The matching
process gives highly successful results on a diverse database of 2D shapes.
