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

研究的目的是分解二维和 三维定常二维时变 流体流动数据集到基本结构的目的， 描述、分析和比较。 从数值获得的结果 计算和现代（光学）诊断往往太 人工检查、操作和显示复杂。 一 需要对这些结果进行更简单但仍然准确的描述， 便于理解数据。 将开发新的方法来自动提取特征 矢量数据。 矢量场，如速度、涡度或 压力梯度被分解成基本结构， 作为临界点和分割流线。 拓扑特征是 它们之间的联系被确认。 该组 要素和连接以图形的形式表示， 可以选择拓扑重要特征的哪些显示 并显示。 这种简化的要素描述还有助于数据 解释，并允许数据库之间的比较， 句法模式识别 这些数据可能是由于大- 规模计算或多通道实验测量。 这项研究的需求正变得越来越迫切， 新型光学三维流动诊断技术的研制 技术和改进的速度和存储能力， 超级计算机 目前开发的数字图像处理和 模式识别算法主要针对二维 意象 将这些技术扩展到高维数据 基地建设需要慎重考虑，不是一件简单的事情。 似乎有望开发自动方法的研究 用于操纵、理解和显示多维 将进行矢量场。

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.