Stable Feature Flow Fields
Stable Feature Flow Fields
复制标题
稳定的特征流场
DOI：
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发表时间：
2011
影响因子：
5.2
通讯作者：
A. Pang
中科院分区：
文献类型：
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作者：
T. Weinkauf;H. Theisel;A. V. Gelder;A. Pang
特征流场是一种广泛接受的提取和跟踪特征的方法。特别是，它们经常被用来跟踪随时间变化的矢量场中的临界点，以及提取和跟踪涡核线。一般的思想是提取的特征或其时间演变使用的流线积分在一个派生的矢量场-所谓的特征流场（FFF）。因此，所需的特征线是FFF的流线。我们将在本文中仔细分析，这条特征线周围的流线可能会偏离它，这会产生一种不稳定的情况：如果积分由于数值误差而稍微偏离特征线，那么它将被发散的邻域捕获，并从真实的特征线中带走。本文的目标是定义一个新的FFF，保证特征线的邻域始终具有收敛行为。通过这种方式，我们可以自动更正数值错误：如果积分稍微偏离要素线，则在正在进行的积分过程中会自动移回要素线。这产生的结果是一个数量级更准确的比以前的计划的结果。我们提出了新的稳定的FFF配方的主要应用程序的跟踪临界点和解决并行向量算子。我们将我们的方法应用于一些数据集。
Feature Flow Fields are a well-accepted approach for extracting and tracking features. In particular, they are often used to track critical points in time-dependent vector fields and to extract and track vortex core lines. The general idea is to extract the feature or its temporal evolution using a stream line integration in a derived vector field-the so-called Feature Flow Field (FFF). Hence, the desired feature line is a stream line of the FFF. As we will carefully analyze in this paper, the stream lines around this feature line may diverge from it. This creates an unstable situation: if the integration moves slightly off the feature line due to numerical errors, then it will be captured by the diverging neighborhood and carried away from the real feature line. The goal of this paper is to define a new FFF with the guarantee that the neighborhood of a feature line has always converging behavior. This way, we have an automatic correction of numerical errors: if the integration moves slightly off the feature line, it automatically moves back to it during the ongoing integration. This yields results which are an order of magnitude more accurate than the results from previous schemes. We present new stable FFF formulations for the main applications of tracking critical points and solving the Parallel Vectors operator. We apply our method to a number of data sets.