Tracking Features in 3D Time Varying Data

Deborah Silver

(Rutgers)

Large distributed time-varying simulations are common in many scientific domains to study the evolution of various phenomena. These simulations produce thousands of timesteps which must be analyzed and interpreted. For datasets with evolving features, feature analysis and visualization tools are crucial to help interpret all the information. For example, it is usually important to know how many regions there are, how are they evolving, how does their volume/mass change, etc. We have developed a methodology for analyzing time-varying datasets which tracks 3D amorphous features as they evolve in time. In this talk, I will present an overview of our methodology and demonstrate how it can provide a paradigm for further investigation of massive datasets.
Thursday 25th November 2004, 14:00
Board Room
Department of Computer Science