Querying for Feature Extraction and Visualization in Climate Modeling
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
CollisionExplorer: a tool for visualizing droplet collisions in a turbulent flow
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Parallel I/O, analysis, and visualization of a trillion particle simulation
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
A classification of scientific visualization algorithms for massive threading
UltraVis '13 Proceedings of the 8th International Workshop on Ultrascale Visualization
| Hi-index | 0.00 |
Feature detection and display are the essential goals of the visualization process. Most visualization software achieves these goals by mapping properties of sampled intensity values and their derivatives to color and opacity. In this work, we propose to explicitly study the local frequency distribution of intensity values in broader neighborhoods centered around each voxel. We have found frequency distributions to contain meaningful and quantitative information that is relevant for many kinds of feature queries. Our approach allows users to enter predicate-based hypotheses about relational patterns in local distributions and render visualizations that show how neighborhoods match the predicates. Distributions are a familiar concept to nonexpert users, and we have built a simple graphical user interface for forming and testing queries interactively. The query framework readily applies to arbitrary spatial data sets and supports queries on time variant and multifield data. Users can directly query for classes of features previously inaccessible in general feature detection tools. Using several well-known data sets, we show new quantitative features that enhance our understanding of familiar visualization results.