Efficient Computation of Combinatorial Feature Flow Fields
Efficient Computation of Combinatorial Feature Flow Fields
复制标题
组合特征流场的高效计算
DOI：
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复制
发表时间：
2012
影响因子：
5.2
通讯作者：
I. Hotz
中科院分区：
文献类型：
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作者：
Jan Reininghaus;Jens Kasten;T. Weinkauf;I. Hotz
我们提出了一种组合算法来跟踪2D时间相关标量字段的临界点。现有的跟踪算法（例如特征流场）采用数据的数值方案，利用数据的导数，这使它们容易发出噪声，并涉及大量的计算参数。相比之下，我们的方法对噪声具有鲁棒性，因为它不需要衍生物，插值和数值集成。此外，我们提出了一个重要的度量，将临界点的空间持久性与其时间进化相结合。这导致了时间感知功能层次结构，这使我们能够将重要的特征与虚假特征区分开。我们的方法仅需要一个易于调整的计算参数，并且自然而然地以核心外部方式进行配制，这可以分析大型数据集。我们将方法应用于计算流体动力学的合成数据和数据集，并将其与稳定的连续特征流场跟踪算法进行比较。
We propose a combinatorial algorithm to track critical points of 2D time-dependent scalar fields. Existing tracking algorithms such as Feature Flow Fields apply numerical schemes utilizing derivatives of the data, which makes them prone to noise and involve a large number of computational parameters. In contrast, our method is robust against noise since it does not require derivatives, interpolation, and numerical integration. Furthermore, we propose an importance measure that combines the spatial persistence of a critical point with its temporal evolution. This leads to a time-aware feature hierarchy, which allows us to discriminate important from spurious features. Our method requires only a single, easy-to-tune computational parameter and is naturally formulated in an out-of-core fashion, which enables the analysis of large data sets. We apply our method to synthetic data and data sets from computational fluid dynamics and compare it to the stabilized continuous Feature Flow Field tracking algorithm.