The feature tree: visualizing feature tracking in distributed AMR datasets
The feature tree: visualizing feature tracking in distributed AMR datasets
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特征树：可视化分布式 AMR 数据集中的特征跟踪
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Lian Jiang
中科院分区：
文献类型：
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作者：
Jing Chen;D. Silver;Lian Jiang
我们描述了一种在分布式计算环境中运行的AMR（自适应网格细化）数据集的特征提取和跟踪算法。因为特性可以跨越多个细化级别和多个处理器，所以必须跨时间、跨级别和跨处理器执行跟踪。结果可视化表示为“特征树”。一个特性包含多个部分，对应于不同层次的细化。特征树允许查看器确定特征在下一个细化级别拆分或合并，并允许查看器提取和隔离多层等值面，并观察该表面如何随时间和空间变化。该算法是在计算导向环境中实现的，这使得可视化例程能够对现场数据进行操作（当模拟正在进行时）。
We describe a feature extraction and tracking algorithm for AMR (adaptive mesh refinement) datasets that operates within a distributed computing environment. Because features can span multiple refinement levels and multiple processors, tracking must be performed across time, across levels, and across processors. The resulting visualization is represented as a "feature tree". A feature contains multiple parts corresponding to different levels of refinements. The feature tree allows a viewer to determine that a feature splits or merges at the next refinement level, and allows a viewer to extract and isolate a multilevel isosurface and watch how that surface changes over both time and space. The algorithm is implemented within a computational steering environment, which enables the visualization routines to operate on the data in-situ (while the simulation is ongoing).