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    2D Vector Field Simplification Based on Robustness

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    Type
    Conference Paper
    Authors
    Skraba, Primoz
    Wang, Bei
    Chen, Guoning
    Rosen, Paul
    KAUST Grant Number
    KUS-C1–016-04
    Date
    2014-03
    Permanent link to this record
    http://hdl.handle.net/10754/597211
    
    Metadata
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    Abstract
    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. © 2014 IEEE.
    Citation
    Skraba P, Bei Wang, Guoning Chen, Rosen P (2014) 2D Vector Field Simplification Based on Robustness. 2014 IEEE Pacific Visualization Symposium. Available: http://dx.doi.org/10.1109/pacificvis.2014.17.
    Sponsors
    We thank Jackie Chen for the combustion datase and Mathew Maltude from LANL and the BER Office of Science UV-CDAT team for the ocean datasets. PR was supported by DOE NETL and KAUST award KUS-C1–016-04. PS was supported by TOPOSYS (FP7-ICT-318493). GC was supported by NSF IIS-1352722. BW was supported in part by INL 00115847 DE-AC0705ID14517 and DOENETL.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2014 IEEE Pacific Visualization Symposium
    DOI
    10.1109/pacificvis.2014.17
    ae974a485f413a2113503eed53cd6c53
    10.1109/pacificvis.2014.17
    Scopus Count
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