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    Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

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    Date
    2021
    Author
    Yan, Lin ORCID
    Masood, Talha Bin ORCID
    Sridharamurthy, Raghavendra ORCID
    Rasheed, Farhan ORCID
    Natarajan, Vijay ORCID
    Hotz, Ingrid ORCID
    Wang, Bei ORCID
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    Abstract
    In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse-Smale complexes play an essential role in capturing the shape of scalar field data. We present a state-of-the-art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time-varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.
    BibTeX
    @article {10.1111:cgf.14331,
    journal = {Computer Graphics Forum},
    title = {{Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization}},
    author = {Yan, Lin and Masood, Talha Bin and Sridharamurthy, Raghavendra and Rasheed, Farhan and Natarajan, Vijay and Hotz, Ingrid and Wang, Bei},
    year = {2021},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14331}
    }
    URI
    https://doi.org/10.1111/cgf.14331
    https://diglib.eg.org:443/handle/10.1111/cgf14331
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA