Introducing topological attributes for objective-based visualization of simulated datasets

Introducing topological attributes for objective-based visualization of simulated datasets
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DOI:
10.2312/vg/vg05/137-145
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发表时间:
2005-06
期刊:
Fourth International Workshop on Volume Graphics, 2005.
影响因子:
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通讯作者:
Yuriko Takeshima;Shigeo Takahashi;I. Fujishiro;G. Nielson
Yuriko Takeshima;Shigeo Takahashi;I. Fujishiro;G. Nielson
中科院分区:
其他
文献类型:
--
作者:
Yuriko Takeshima;Shigeo Takahashi;I. Fujishiro;G. Nielson

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最近的发展,在设计多维传递函数,使我们能够自动生成可理解的可视化图像的给定体积,考虑到当地的特点,如微分和曲率。然而,特别是当可视化的体积获得的科学模拟,观察者通常利用他们的知识的模拟设置作为线索,以有效地控制可视化参数为自己的特定目的。因此,本文提出了一个基于对象的框架,通过引入一组新的拓扑属性,模拟体数据集的可视化。这些拓扑属性是从给定体积数据集的水平集图中计算出来的,因此与传统的局部属性不同,因为它们也照亮了体积的全局结构。本框架提供了一个系统的手段,强调底层的体积功能,如嵌套结构的等值面,等值面轨迹的配置,和等值面的拓扑类型的过渡。几种组合的拓扑属性连同相关的传递函数设计,并应用到真实的模拟数据集,以证明本框架的可行性。
Recent development in the design of multi-dimensional transfer functions allows us to automatically generate comprehensible visualization images of given volumes by taking into account local features such as differentials and curvatures. However, especially when visualizing volumes obtained by scientific simulations, observers usually exploit their knowledge about the simulation settings as the clues to the effective control of visualization parameters for their own specific purposes. This paper therefore presents an objective-based framework for visualizing simulated volume datasets by introducing a new set of topological attributes. These topological attributes are calculated from the level-set graph of a given volume dataset, and thus differ from the conventional local attributes in that they also illuminate the global structure of the volume. The present framework provides a systematic means of emphasizing the underlying volume features, such as nested structures of isosurfaces, configuration of isosurface trajectories, and transitions of isosurface's topological type. Several combinations of the topological attributes together with the associated transfer function designs are devised and applied to real simulated datasets in order to demonstrate the feasibility of the present framework.