Visualizing and tracking features in 3d time-varying datasets
Xin Wang, Deborah E. Silver · 1999
Visualizing 3D time-varying datasets is difficult because of the immense amount of data to be processed and understood. This is especially true when the datasets are turbulent with many evolving amorphous regions, as it is difficult to observe patterns and visually follow regions of interest. Standard visualization tools provide no facilities for manipulating features in the dataset. Better visualization and quantification techniques are required to ease the process of understanding large datasets. Visualization tools help scientists sharpen their intuition and can have strong impact on scientific discoveries. Our research is motivated to develop efficient algorithms and visualization techniques to deal the massive 3D time-varying datasets. Our research is a feature based approach which allows users to extract regions, then visualize, track, isolate and quantify their evolution. The algorithms can work on both structured and unstructured datasets, and could be easily extended to handle other grid types. Once features are identified and tracked, features of interest can be isolated, and properties of the features and their evolutionary history can be computed. Finally, the tracking information and quantitative information can be used to enhance the visualization of 3D time-varying datasets. Feature-based approaches can significantly improve and facilitate the processing of massive datasets. We demonstrate these techniques on datasets from various application domains to show generality of our techniques. We have implemented feature tracking systems that allow scientists to apply our techniques to visualize their time-varying datasets. We expect that our research will provide useful tools for analyzing features and improving the visualization of time-varying scientific datasets.