Shape analysis is playing an increasingly important role in many applications
where object classification and understanding are of interest. Solutions to many
existing as well as new emerging applied problems (e.g, object recognition,
biometrics etc.) crucially depend on object modeling and their parsimonious
representation. Modeling an active silhouette in a video sequence provides a
good solution for activity surveillance. We pose this problem as one of tracking
a flow of shapes as entities on a curved space. We first propose a stochastic
model for a flow on a manifold to carry out classification of different
processes. We then exploit this insight to develop a tracking filter of these
shapes and subsequently propose a generative model useful in a variety of
applications. We subsequently propose a generative model for human activity. We
provide substantiating illustrations.

Short Biography: Hamid Krim received his BSc.and MSc. in EE from University of
Washington and a Ph.D. degree in ECE from Northeastern University. He was a
Member of Technical Staff at AT&T Bell Labs, where he has conducted research and
development in the areas of telephony and digital communication
systems/subsystems. Following an NSF postdoctoral fellowship at Foreign Centers
of Excellence, LSS/University of Orsay, Paris, France, he joined the Laboratory
for Information and Decision Systems, Massachusetts Institute of Technology,
Cambridge, MA as a Research Scientist and where he was performing and
supervising research. He is presently Professor of Electrical Engineering in the
ECE Department, North Carolina State University, Raleigh, directing the Vision,
Information and Statistical Signal Theories and Applications group. He is a
Fellow of the IEEE