Authors
Markus Hadwiger, VRVis Research Center, Austria
Fritz Laura, VRVis Research Center, Austria
Christof Rezk-Salama, University of Siegen, Germany
Thomas Höllt, VRVis Research Center, Austria
Georg Geier, Austrian Foundry Research Institute
Thomas Pabel, Austrian Foundry Research Institute
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.147
Abstract
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.
Citation
Markus Hadwiger, Fritz Laura, Christof Rezk-Salama, Thomas Höllt, Georg Geier, Thomas Pabel, “Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, pp. 1507-1514, Nov/Dec, 2008