Feature tracking & visualization in 'VisIt'

The study and analysis of large experimental or simulation datasets in the field of science and engineering pose a great challenge to the scientists. These complex simulations generate data varying over a period of time. Scientists need to glean large quantities of time-varying data to understand the underlying physical phenomenon. This is where visualization tools can assist scientists in their quest for analysis and understanding of scientific data. Feature Tracking, developed at Visualization & Graphics Lab (Vizlab), Rutgers University, is one such visualization tool. Feature Tracking is an automated process to isolate and analyze certain regions or objects of interest, called 'features' and to highlight their underlying physical processes in time-varying 3D datasets.;In this thesis, we present a methodology and documentation on how to port 'Feature Tracking' into VisIt. VisIt is a freely available open-source visualization software package that has a rich feature set for visualizing and analyzing data. VisIt can successfully handle massive data quantities in the range of tera-scale. The technology covered by this thesis is an improvement over the previous work that focused on Feature Tracking in VisIt. In this thesis, the emphasis is on the visualization of features by assigning a constant color to the features (or objects) that move (or change their shape) over a period of time. Our algorithm gives scientists an option to choose only the features of interest amongst all the extracted objects. Scientists can then focus their attention solely on those objects that could help them in understanding the underlying mechanism better. We tested our algorithm on various datasets and present the results in this thesis.