Neuroth Tyson, Rieth Martin, Aditya Konduri, Lee Myoungkyu, Chen Jacqueline H, Ma Kwan-Liu
IEEE Trans Vis Comput Graph. 2023 Jan;29(1):548-558. doi: 10.1109/TVCG.2022.3209473. Epub 2022 Dec 16.
Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features.
由于尺度、复杂性和遮挡问题，对多变量体积数据进行空间统计分析可能具有挑战性。拓扑分割、特征提取和统计汇总方面的进展有助于克服这些挑战。这项工作引入了一种新的空间统计分解方法，该方法基于水平集、连通分量以及受限质心 Voronoi 镶嵌的一种新颖变体，这种变体更适合空间统计分解和并行效率。生成的数据结构将特征组织成一个连贯的嵌套层次结构，以支持灵活高效的核外感兴趣区域提取。接下来，我们提供了一种高效的并行实现。最后，设计了一个基于此方法的交互式可视化系统，并将其应用于湍流燃烧数据。这种组合方法通过自上而下的方法，通过多层次细节实现了大规模数据的交互式空间统计分析工作流程，将相空间统计与空间特征联系起来。
