RIA: Data Reduction and New Visualization Techniques for Three-Dimensional Data Sets

RIA：三维数据集的数据缩减和新可视化技术

• 批准号: 9210439
• 负责人: Bernd Hamann
• 金额: $ 11.5万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9210439&HistoricalAwards=false
• 关键词: RIA; Data; Reduction; New; Visualization

This research is concerned with data reduction and general visualization techniques for scalar-valued 3D data sets. Many practical applications deal with 3D data sets where points in space, with associated function values, are given. Such data sets occur in medicine (tomography), geosciences and physical sciences (temperature, pressure, radiation), and several other sciences and disciplines of engineering measurements or mathematical simulations. Generally, such data sets are huge, aggravating the tasks of data storage, interactive data manipulation, and real-time data visualization. Discrete scalar-valued data sets can be reduced by considering derivative information at the given data points. Curvature information of graphs of functions locally approximating the data can be used to define a measure of the significance of single data points can be weighted and removed iteratively form the 3D point set. The proposed methods for 3D points, and 3D retriangulation strategies. The result of reduction algorithm is a data set consisting of scattered 3D points and an associated triangulation (tetrahedra). Common visualization techniques require structured data sets, points typically being organized in a rectilinear ("cuberille") grid. This research will introduce visualization methodologies allowing discretized scalar fields, with arbitrarily located data points, to be rendered.

这项研究与标量值3D数据集的数据降低和一般可视化技术有关。 许多实用应用程序涉及3D数据集，其中给出了空间点（带有相关函数值的点）。 此类数据集发生在医学（断层扫描），地球科学和物理科学（温度，压力，辐射）以及工程测量或数学模拟的其他几项科学和学科中。 通常，此类数据集巨大，加剧了数据存储，交互式数据操作和实时数据可视化的任务。 可以通过在给定数据点考虑衍生信息来减少离散标量值数据集。 局部近似数据的函数图的曲率信息可用于定义单个数据点的重要性的度量，可以加权并迭代地构成3D点集。 提出的3D点和3D重新调节策略的方法。 还原算法的结果是一个数据集，该数据集由散射的3D点和相关的三角剖分（四面体）组成。 通用可视化技术需要结构化的数据集，该点通常在直线（“ Cuberille”）网格中组织。 这项研究将介绍可视化方法，允许渲染具有任意位置的数据点的离散标量字段。