Hadwiger Markus, Laura Fritz, Rezk-Salama Christof, Höllt Thomas, Geier Georg, Pabel Thomas
VRVis Research Center, Austria.
IEEE Trans Vis Comput Graph. 2008 Nov-Dec;14(6):1507-14. doi: 10.1109/TVCG.2008.147.
This paper presents a novel method for interactive exploration of industrial CT volumes such as cast metal parts, with the goal of interactively detecting, classifying, and quantifying features using a visualization-driven approach. The standard approach for defect detection builds on region growing, which requires manually tuning parameters such as target ranges for density and size, variance, as well as the specification of seed points. If the results are not satisfactory, region growing must be performed again with different parameters. In contrast, our method allows interactive exploration of the parameter space, completely separated from region growing in an unattended pre-processing stage. The pre-computed feature volume tracks a feature size curve for each voxel over time, which is identified with the main region growing parameter such as variance. A novel 3D transfer function domain over (density, feature size, time) allows for interactive exploration of feature classes. Features and feature size curves can also be explored individually, which helps with transfer function specification and allows coloring individual features and disabling features resulting from CT artifacts. Based on the classification obtained through exploration, the classified features can be quantified immediately.
本文提出了一种用于交互式探索工业CT体数据（如铸造金属部件）的新方法，其目标是使用可视化驱动的方法交互式地检测、分类和量化特征。缺陷检测的标准方法基于区域生长，这需要手动调整参数，如密度和尺寸的目标范围、方差以及种子点的指定。如果结果不令人满意，则必须使用不同的参数再次执行区域生长。相比之下，我们的方法允许对参数空间进行交互式探索，在无人参与的预处理阶段与区域生长完全分离。预先计算的特征体数据会随时间跟踪每个体素的特征尺寸曲线，该曲线与主要区域生长参数（如方差）相关联。一种新颖的三维传递函数域（密度、特征尺寸、时间）允许对特征类别进行交互式探索。特征和特征尺寸曲线也可以单独进行探索，这有助于传递函数的指定，并允许对单个特征进行着色以及禁用由CT伪影产生的特征。基于通过探索获得的分类，可以立即对分类后的特征进行量化。
