Continuous Representation of Projected Attribute Spaces of Multifields over Any Spatial Sampling
Continuous Representation of Projected Attribute Spaces of Multifields over Any Spatial Sampling
复制标题
DOI：
10.1111/cgf.12117
复制
发表时间：
2013-06
影响因子：
2.5
通讯作者：
V. Molchanov;A. Fofonov;L. Linsen
中科院分区：
文献类型：
--
作者：
V. Molchanov;A. Fofonov;L. Linsen
对于多维数据的可视化分析，通常使用降维方法来投影到低维的视觉空间。在多域的背景下，即具有多维属性空间的体数据，可以利用体域中样本的空间排列来生成投影属性空间的连续表示（CoRPAS）。在这里，体积域中的样本位置可以以结构化或非结构化的方式排列，可以通过网格或网格连接也可以不连接。我们提出了一种使用各向同性密度函数为任何样本排列生成CoRPAS的方法。提出了一种由体可视化、CoRPAS和基于星点坐标的交互部件组成的交互式视觉探索系统。星形坐标小部件为用户提供了一种直观的方法来改变投影矩阵。协调视图允许以刷刷和链接的形式进行特征选择。该方法既适用于合成数据，也适用于物理现象的数值模拟数据。特别是，基于光滑粒子流体动力学的仿真被解决，其中仿真核可以用来产生与仿真一致的CoRPAS。我们还展示了如何支持CoRPAS中属性值的对数缩放，这具有很高的实际意义。
For the visual analysis of multidimensional data, dimension reduction methods are commonly used to project to a lower‐dimensional visual space. In the context of multifields, i.e., volume data with a multidimensional attribute space, the spatial arrangement of the samples in the volumetric domain can be exploited to generate a Continuous Representation of the Projected Attribute Space (CoRPAS). Here, the sample locations in the volumetric domain may be arranged in a structured or unstructured way and may or may not be connected by a grid or a mesh. We propose an approach to generate CoRPAS for any sample arrangement using an isotropic density function. An interactive visual exploration system with three coordinated views of volume visualization, CoRPAS, and an interaction widget based on star coordinates is presented. The star‐coordinates widget provides an intuitive means for the user to change the projection matrix. The coordinated views allow for feature selection in form of brushing and linking. The approach is applied to both synthetic data and data resulting from numerical simulations of physical phenomena. In particular, simulations based on Smoothed Particle Hydrodynamics are addressed, where the simulation kernel can be used to produce a CoRPAS that is consistent with the simulation. We also show how a logarithmic scaling of attribute values in CoRPAS is supported, which is of high practical relevance.