Title: Object Topology Characterization and Comparison

Professor H. Krim, ECE Dept., North Carolina State University, Raleigh, NC

Characterization and recognition of shapes is of central importance in computer vision.

Shape study has also taken on a new significance in computer graphics and multimedia applications as many tasks

crucially rely on much of the information. In this talk, we describe a new approach for object matching based on a global geodesic measure.
The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures
the global geodesic distance between two arbitrary points on the surface of an object. In contrast to the Euclidean distance which is more suitable
for linear spaces, the geodesic distance has the advantageous ability  to capture the intrinsic geometric structure of the data. The matching task
therefore becomes a one-dimensional comparison problem between probability distributions hence greatly simplifying the comparison of 3D structures.
Object matching is then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions,
which is computationally efficient and inexpensive to obtain.