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Mcloughlin et al.: similarity measures for enhancing interactive streamline seeding 1 similarity measures for enhancing interactive streamline seeding.
| Content Provider | CiteSeerX |
|---|---|
| Author | Mcloughlin, Tony Jones, Mark W. Laramee, Robert S. Malki, Rami Hansen, Charles D. |
| Abstract | Abstract—Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of Euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a non-uniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine-tune the clustering results at run-time while avoiding any time-consuming re-computation. Our method maintains interactive rates even when hundreds of streamlines are used. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Similarity Measure Enhancing Interactive Streamline Seeding Original Streamline Vector Field Visualization Previous Method Integral Curve Clustering Result Novel Scheme Present New Approach Euclidean Distance Test Good Clustering Result Abstract Streamline Seeding Rake High Visual Complexity Computational Expense Overall Flow Behavior Seeding Object Similarity Distance Measure Novel Idea Non-uniform Seeding Distribution Similarity-based Clustering Enables Vast Number Streamline Signature Time-consuming Re-computation Streamline Seeding Rake Curve-based Attribute Focus Context Derived Signature Small Subset High Level Popular Statistical Measure Similarity Comparison Compact Representation Interactive Rate Hierarchical Variant |
| Content Type | Text |
