自动的,张量引导的说明性向量场可视化

Cornelia Auer, Jens Kasten, A. Kratz, E. Zhang, I. Hotz
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引用次数: 6

摘要

本文提出了一种矢量场可视化方法,它模拟了一种类似草图的表示。可视化结合了两个主要视角:基于高度简化的场作为背景可视化的大规模趋势,以及在其精确位置突出强烈表达特征的局部可视化。每个分量考虑向量场本身及其空间导数。导数是一个非对称张量场,它允许演绎标量,反映独特的场性质,如旋转或剪切强度。背景可视化的基础是矢量和标量聚类方法。局部特征被定义为各自标量场的极值。标量场拓扑的应用为特征提取提供了深刻的数学基础。所有的设计决策都以生成易于阅读的可视化为目标。为了证明我们方法的有效性,我们展示了具有不同复杂性和特征的三个不同数据集的结果。
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Automatic, tensor-guided illustrative vector field visualization
This paper proposes a vector field visualization, which mimics a sketch-like representation. The visualization combines two major perspectives: Large scale trends based on a strongly simplified field as background visualization and a local visualization highlighting strongly expressed features at their exact position. Each component considers the vector field itself and its spatial derivatives. The derivate is an asymmetric tensor field, which allows the deduction of scalar quantities reflecting distinctive field properties like strength of rotation or shear. The basis of the background visualization is a vector and scalar clustering approach. The local features are defined as the extrema of the respective scalar fields. Applying scalar field topology provides a profound mathematical basis for the feature extraction. All design decisions are guided by the goal of generating a simple to read visualization. To demonstrate the effectiveness of our approach, we show results for three different data sets with different complexity and characteristics.
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