Visualizing and Tracking Evolving Features in 3D Unstructured and Adaptive Datasets
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.
- Research Organization:
- Rutgers Univ., Piscataway, NJ (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- FG02-98ER25364
- OSTI ID:
- 948554
- Report Number(s):
- DOE/ER/25364-1; TRN: US200911%%406
- Country of Publication:
- United States
- Language:
- English
Similar Records
Visualization and quantification of evolving datasets. Final report: 8-1-93 - 4-30-97
Parallel In Situ Coupling of a Simulation with a Fully Featured Visualization System
A Confidence-Guided Technique for Tracking Time-Varying Features
Technical Report
·
Tue Jul 20 00:00:00 EDT 1999
·
OSTI ID:948554
Parallel In Situ Coupling of a Simulation with a Fully Featured Visualization System
Conference
·
Sat Jan 01 00:00:00 EST 2011
·
OSTI ID:948554
A Confidence-Guided Technique for Tracking Time-Varying Features
Journal Article
·
Mon Mar 01 00:00:00 EST 2021
· Computing in Science and Engineering
·
OSTI ID:948554