A Similarity Evaluation Method for Volume Data Sets by Using Critical Point Graph
A Similarity Evaluation Method for Volume Data Sets by Using Critical Point Graph
复制标题
一种利用临界点图的体数据集相似度评价方法
DOI：
10.1007/978-3-540-77704-5_28
复制
发表时间：
2005
期刊：
影响因子：
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通讯作者：
K. Koyamada
中科院分区：
文献类型：
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作者：
Tomoki Minami;Koji Sakai;K. Koyamada
计算机模拟的日益广泛的应用，相应地增加了对大量计算结果的分类或在给定数据库中搜索任意数据集的有效方法的需求。为了对计算模拟结果进行分类或搜索，需要评估给定数据相对于数据库中的参考数据之间的相似性。以“临界点图（CPG）”为指标的相似度估计方法已被证明是有效的，但该方法不支持旋转或缩放等变换操作。在本文中，我们提出了一种基于CPG的相似性估计方法，支持旋转和缩放变换的二维和三维标量数据集（体数据集）。我们可以证实它的有效性，也证明了上级优于传统的轮廓树（CT）的匹配技术，使用仿射不变的度量。还讨论了如何正确使用这些匹配技术，以澄清其优点和缺点。
The ever increasing use of computer simulation has proportionately increased the demands for an efficient method for classification of a large amount of computational results or for searching an arbitrary data set in a given database. In order to classify or to search for a computational simulation result, it is necessary to evaluate the similarity between a given data in respect to the reference data in a database. A similarity estimation method which employs ”Critical Point Graph (CPG)” as an index has proven effective, however this method does not support transformation operations such as rotation or scaling. In this paper, we propose a CPG-based similarity estimation method supporting both rotation and scaling transformations for two and three dimensional scalar data sets (volume data sets). We could confirm its effectiveness, and also proved superior to the traditional Contour Tree (CT) based matching technique which uses affine-invariant metrics. Some discussion about the proper use of these matching techniques is also presented to clarify the advantages and disadvantages.