Approximation Techniques in Geometric Computing

Jyh-Ming Lien, PhD
Department of Computer Science
Texas A&M University
http://parasol.tamu.edu/groups/amatogroup/research

Abstract

Geometric computations are essential in many real-world problems. One important issue in these geometric computations is that the geometric models in these problems can be very large so that computations on them require infeasible storage or computation time. We have developed approximate techniques for handling problems with large geometries. Our study of a wide range of applications shows in addition to providing computational efficiency, our techniques also provide natural multi-resolution or hierarchical representations. Our techniques also provide a mechanism to focus on key structural features and ignore less significant artifacts such as wrinkles and surface texture. Our work has attracted a wide range of interest from the academic community and industry.
This talk will provide an overview of our methods and their applications in shape representation, motion generation and behavior simulation. We will describe in more detail our technique for a general shape approximation method we call Approximate Convex Decomposition (ACD), and some examples of its many potential applications. More information about this work can be found on our web pages. http://parasol.tamu.edu/groups/amatogroup/research/

Candidate Bio

Jyh-Ming Lien is a Ph.D. student in the Department of Computer Science at Texas A&M University working with Dr. Nancy Amato. He is a member of the Algorithms & Applications Group in the Parasol Lab. His research is in the areas of computational geometry, robotics motion planning and computer graphics. He received his B.S. in Computer Science from National ChengChi University, Taiwan, in 1999. Information about his research and many of his publications can be found at: http://parasol.tamu.edu/~neilien/