2D vector field simplification based on robustness

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Publication Type pre-print
School or College <blank>
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Creator Rosen, Paul Andrew
Other Author Skraba, Primoz; Wang, Bei; Chen, Guoning
Title 2D vector field simplification based on robustness
Date 2014-01-01
Description Vector field simplification aims to reduce the complexity of the flow by removing features in order of their relevance and importance, to reveal prominent behavior and obtain a compact representation for interpretation. Most existing simplification techniques based on the topological skeleton successively remove pairs of critical points connected by separatrices, using distance or area-based relevance measures. These methods rely on the stable extraction of the topological skeleton, which can be difficult due to instability in numerical integration, especially when processing highly rotational flows. These geometric metrics do not consider the flow magnitude, an important physical property of the flow. In this paper, we propose a novel simplification scheme derived from the recently introduced topological notion of robustness, which provides a complementary view on flow structure compared to the traditional topological-skeleton-based approaches. Robustness enables the pruning of sets of critical points according to a quantitative measure of their stability, that is, the minimum amount of vector field perturbation required to remove them. This leads to a hierarchical simplification scheme that encodes flow magnitude in its perturbation metric. Our novel simplification algorithm is based on degree theory, has fewer boundary restrictions, and so can handle more general cases. Finally, we provide an implementation under the piecewise-linear setting and apply it to both synthetic and real-world datasets.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 49
Last Page 56
Language eng
Bibliographic Citation Skraba, P., Wang, B., Chen, G., & Rosen, P. (2014). 2D vector field simplification based on robustness. IEEE Pacific Visualization Symposium, 6787136, 49-56.
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Format Extent 4,583,907 bytes
Identifier uspace,18684
ARK ark:/87278/s6sn3k28
Setname ir_uspace
Date Created 2014-05-19
Date Modified 2021-05-06
ID 712556
Reference URL https://collections.lib.utah.edu/ark:/87278/s6sn3k28