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Trajectory-based Flow Feature Tracking in Joint Particle/Volume Datasets


The ability to isolate and track time-varying volumentric features of interest allows domain scientists to better manage large complex datasets both in terms of visual understanding and computational efficiency. This work presents a new trajectory-based feature tracking technique for use in joint particle/volume datasets. While traditional feature tracking approaches generally require a high temporal resolution, this method utilizes the indexed trajectories of corresponding Lagrangian particle data to efficiently track features over large jumps in time. Such a technique is especially useful for situations where the volume dataset is either temporally sparse or too large to efficiently track a feature through all intermediate timesteps. We demonstrate the effectiveness of this technique using real world combustion and atmospheric datasets and compare it to existing tracking methods to justify its advantages and accuracy.



Researchers

Franz Sauer is a Postdoctoral Scholar at the University of California, Davis, studying computer science and scientific visualization under Kwan-Liu Ma. His research interests include data visualization, large-scale scientific simulations, computer graphics, and physics. Before joining the ViDi group, Franz received a B.S. in physics from the California Institute of Technology.