Visualizing time-varying features with TAC-based distance fields
Visualizing time-varying features with TAC-based distance fields
复制标题
使用基于 TAC 的距离场可视化时变特征
DOI：
10.1109/pacificvis.2009.4906831
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Han
中科院分区：
文献类型：
--
作者：
Teng;Han
为了分析随时间变化的数据集，跟踪随时间变化的特征对于更好地理解底层物理过程的动态性质通常是必要的。然而，当不能容易地定义特征的边界时，跟踪3D时变特征并不是微不足道的。在本文中，我们提出了一种新的框架来可视化时变特征及其运动，而不需要明确的特征分割和跟踪。在我们的框架中，时变特征用时间序列或时间活动曲线(TAC)来描述。为了计算体素的时间序列和特征之间的距离或相似性，我们使用动态时间扭曲(DTW)距离度量。DTW的目的是比较两个时间序列之间的形状相似性和时间的最佳翘曲，以便考虑特征在时间上的相移。在应用DTW将每个体素的时间序列与特征进行比较后，可以计算出时不变的距离场。每个体素匹配该特征所需的时间扭曲量提供了该特征最可能发生的时间的估计。基于基于TAC的距离场，可以推导出几种可视化方法来突出特征的位置和运动。我们提供了几个案例研究来演示和比较我们的框架的有效性。
To analyze time-varying data sets, tracking features over time is often necessary to better understand the dynamic nature of the underlying physical process. Tracking 3D time-varying features, however, is non-trivial when the boundaries of the features cannot be easily defined. In this paper, we propose a new framework to visualize time-varying features and their motion without explicit feature segmentation and tracking. In our framework, a time-varying feature is described by a time series or Time Activity Curve (TAC). To compute the distance, or similarity, between a voxel's time series and the feature, we use the Dynamic Time Warping (DTW) distance metric. The purpose of DTW is to compare the shape similarity between two time series with an optimal warping of time so that the phase shift of the feature in time can be accounted for. After applying DTW to compare each voxel's time series with the feature, a time-invariant distance field can be computed. The amount of time warping required for each voxel to match the feature provides an estimate of the time when the feature is most likely to occur. Based on the TAC-based distance field, several visualization methods can be derived to highlight the position and motion of the feature. We present several case studies to demonstrate and compare the effectiveness of our framework.