Automated 3-D Feature Extraction from 3-D Data Sets

研究的目的是将二维和三维稳态以及二维随时间变化的流体流动数据集分解为基本结构，以便于描述、分析和比较。 从数值计算和现代（光学）诊断获得的结果通常过于复杂，无法进行手动检查、操作和显示。 为了促进数据理解，需要对这些结果进行更简单但仍然准确的描述。 将开发新方法用于从矢量数据中自动提取特征。 速度、涡度或压力梯度等矢量场将被分解为临界点和分割流线等基本结构。 拓扑特征被识别并确定它们之间的连接。 特征和连接的集合以图形的形式表示，可以从中选择并显示拓扑上重要的特征的显示。 这种简化的特征描述还有助于数据解释，并允许使用句法模式识别在数据库之间进行比较。 这些数据可能来自大规模计算或多通道实验测量。 随着新型光学三维流诊断技术的发展以及超级计算机速度和存储能力的提高，对这项研究的需求变得越来越迫切。 目前开发的数字图像处理和模式识别算法主要针对二维图像。 将这些技术扩展到更高维度的数据库需要仔细考虑，这不是一项简单的任务。 将开展有望开发用于操纵、理解和显示多维矢量场的自动方法的研究。

The objective of the research is to decompose two-dimensional and three-dimensional steady state and two-dimensional time dependent fluid flow data sets into elementary structures for purposes of description, analysis and comparison. Results obtained from numerical calculations and modern (optical) diagnostics are often too complicated for manual inspection, manipulation and display. A simpler but still accurate description of these results is needed to facilitate data understanding. New methods will be developed for automatic extraction of features from vector data. Vector fields such as velocity, vorticity or pressure gradient are to be decomposed into elementary structures such as critical points and dividing streamlines. Topological features are recognized and the connections between them determined. The set of features and connections are represented in the form of a graph, from which displays of topologically significant features can be selected and displayed. This simplified feature description also aids data interpretation and allows comparison between data bases using syntactic pattern recognition. Such data can be both due to large- scale computations or multi-channel experimental measurements. The need for this research is becoming increasingly urgent with the development of new optical three-dimensional flow diagnostic techniques and improved speed and storage capability of supercomputers. Currently developed digital image processing and pattern recognition algorithms are mainly aimed at two-dimensional imagery. Extension of these techniques to higher dimensional data bases needs to be carefully considered and is not a simple task. Research that appears to hold promise for developing automatic methods for the manipulation, understanding and display of multi-dimensional vector fields will be carried out.