Tracking scalar features in unstructured data sets
Tracking scalar features in unstructured data sets
复制标题
跟踪非结构化数据集中的标量特征
DOI：
10.1109/visual.1998.745288
复制
发表时间：
1998
期刊：
影响因子：
--
通讯作者：
Xin Wang
中科院分区：
文献类型：
--
作者：
D. Silver;Xin Wang
3D时变非结构化和结构化数据集由于涉及大量数据而难以可视化和分析。这些数据集包含许多不断演变的无定形区域，标准的可视化技术无法帮助科学家跟踪感兴趣的区域。在本文中，我们提出了一个基本框架的可视化时变数据集，和一个新的算法和数据结构来跟踪体特征的非结构化标量数据集。该算法和数据结构具有通用性，可适用于结构网格、曲线网格、自适应网格和混合网格。跟踪的特征可以是任何类型的连接区域。例子显示从正在进行的研究。
3D time-varying unstructured and structured data sets are difficult to visualize and analyze because of the immense amount of data involved. These data sets contain many evolving amorphous regions, and standard visualization techniques provide no facilities to aid the scientist to follow regions of interest. In this paper, we present a basic framework for the visualization of time-varying data sets, and a new algorithm and data structure to track volume features in unstructured scalar data sets. The algorithm and data structure are general and can be used for structured, curvilinear, adaptive and hybrid grids as well. The features tracked can be any type of connected regions. Examples are shown from ongoing research.