Visualizing and Tracking Evolving Features in 3D Unstructured and Adaptive Datasets

Silver, D. ; Zabusky, N.

Rutgers University

关键词: Computer Graphics;    Data Analysis;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Scientific Visualization, Computational Fluid Dynamics, Feature Tracking, Distributed Computing;    Implementation;   

DOI  :  10.2172/948554
RP-ID  :  DOE/ER/25364-1
RP-ID  :  FG02-98ER25364
RP-ID  :  948554

美国|英语

来源: UNT Digital Library

【 摘 要 】

The massive amounts of time-varying datasets being generated demand new visualization and quantification techniques. Visualization alone is not sufficient. Without proper measurement information/computations real science cannot be done. Our focus is this work was to combine visualization with quantification of the data to allow for advanced querying and searching. As part of this proposal, we have developed a feature extraction adn tracking methodology which allows researcher to identify features of interest and follow their evolution over time. The implementation is distributed and operates over data In-situ: where it is stored and when it was computed.