Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

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dc.contributor.authorYan, Linen_US
dc.contributor.authorUllrich, Paul Aaronen_US
dc.contributor.authorVan Roekel, Luke P.en_US
dc.contributor.authorWang, Beien_US
dc.contributor.authorGuo, Hanqien_US
dc.contributor.editorHauser, Helwig and Alliez, Pierreen_US
dc.date.accessioned2023-10-06T11:58:55Z
dc.date.available2023-10-06T11:58:55Z
dc.date.issued2023
dc.description.abstractCritical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.en_US
dc.description.number6
dc.description.sectionheadersORIGINAL ARTICLES
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14799
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14799
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14799
dc.publisher© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectcritical point tracking
dc.subjecttopological data analysis
dc.subjecttopology in visualization
dc.subjectvector field topology
dc.titleMultilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparisonen_US
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