Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison
Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison
复制标题
二维矢量场特征跟踪、选择和比较的多级鲁棒性
DOI：
10.1111/cgf.14799
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发表时间：
2023
影响因子：
2.5
通讯作者：
Guo, Hanqi
中科院分区：
文献类型：
--
作者：
Yan, Lin;Ullrich, Paul Aaron;Van Roekel, Luke P.;Wang, Bei;Guo, Hanqi
临界点跟踪是科学可视化中的一个核心主题，用于理解时变向量场数据的动态行为。最近引入了鲁棒性的拓扑概念来量化临界点的结构稳定性，即临界点的鲁棒性是使其被抵消所需的对向量场的最小扰动量。将临界点跟踪与鲁棒性概念联系起来的理论基础已经建立，特别地，临界点可以基于它们在稳定性上的接近性来跟踪，通过鲁棒性来衡量，而不仅仅是域内的距离接近度。然而，在实践中，经典的鲁棒性的计算可能会产生文物时，临界点是接近域的边界，因此，我们没有一个完整的图片的向量场的行为在其局部邻域。为了缓解这些问题，我们引入了一个多层次的鲁棒性框架的研究二维时变向量场。我们计算的鲁棒性的临界点在不同的邻域捕捉数据的多尺度性质，并减轻经典的鲁棒性计算所遭受的边界效应。我们通过实验证明，这种新的鲁棒性概念可以与现有的特征跟踪算法无缝结合，以提高向量场在大规模科学模拟的特征跟踪，选择和比较方面的视觉可解释性。我们第一次观察到，最小多级鲁棒性与领域科学家在研究真实的世界热带气旋数据集时使用的物理量高度相关。这样的观察有助于增加稳健性的物理可解释性。
Critical 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.
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DOI：
--
发表时间：
2011
期刊：
Topological Methods in Data Analysis and Visualization
影响因子：
--
作者：
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影响因子：
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作者：
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影响因子：
3
作者：
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通讯作者：
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DOI：
10.1109/tvcg.2016.2534538
发表时间：
2016
影响因子：
5.2
作者：
P. Skraba;P. Rosen;Bei Wang;Guoning Chen;H. Bhatia;Valerio Pascucci
通讯作者：
Valerio Pascucci
影响因子：
4.1
作者：
H. Edelsbrunner;D. Morozov;A. Patel
通讯作者：
A. Patel