posted on 1997-01-01, 00:00authored byDongmei Zhang, Martial Hebert
We describe an approach to the classification of 3-D objects
using a multi-scale representation. This approach
starts with a smoothing algorithm for representing objects
at different scales. Smoothing is applied in curvature space
directly, thus avoiding the usual shrinkage problems and allowing
for efficient implementations. A 3-D similarity measure
that integrates the representations of the objects at
multiple scales is introduced. Given a library of models, objects
that are similar based on this multi-scale measure are
grouped together into classes. The objects that are in the
same class are combined into a single prototype object. Finally,
the prototypes are used for hierarchical recognition
by first comparing the scene representation to the prototypes
and then matching it only to the objects in the most likely
class rather than to the entire library of models. Beyond its
application to object recognition, this approach provides an
attractive implementation of the intuitive notions of scale
and approximate similarity for 3-D shapes.