Muelder and Ma: Rapid Feature Extraction and Tracking through Region Morphing

The ability to extract and follow time-varying features in volume data obtained from large-scale numerical simulations makes it possible for scientists to see and validate the modeled phenomena and processes because the extracted features generally take much less space to store and less computing resources to visualize. Previous feature extraction and tracking methods extract features in each time step independently, and then find the corresponding ones in the following time steps of the data. These methods typically do not exploit information in the time dimension. In this paper, we present a new method that is based on a prediction and morphing approach, which makes use of temporal-space information and inherently solves the correspondence problem that previous methods have difficulties with. Our method is low cost, and thus facilitates interactive feature selection, visualization, and refinement, unlike previous methods which were largely designed or suited for batch-mode processing due to their high computational cost.