Talks and Poster Presentations (with Proceedings-Entry):

J. Weissenböck, A. Amirkhanov, E. Gröller, J. Kastner, Ch. Heinzl et al.:
"PorosityAnalyzer: Visual Analysis and Evaluation of Segmentation Pipelines to Determine the Porosity in Fiber-Reinforced Polymers";
Talk: VAST, Baltimore; 2016-10-23 - 2016-10-28; in: "Proceedings VAST 2016", (2016), 101 - 110.

English abstract:
In this paper we present PorosityAnalyzer, a novel tool for detailed evaluation and visual analysis of pore segmentation pipelines to determine the porosity in fiber-reinforced polymers (FRPs). The presented tool consists of two modules: the computation module and the analysis module. The computation module enables a convenient setup and execution of distributed off-line-computations on industrial 3D X-ray computed tomography datasets. It allows the user to assemble individual segmentation pipelines in the form of single pipeline steps, and to specify the parameter ranges as well as the sampling of the parameter-space of each pipeline segment. The result of a single segmentation run consists of the input parameters, the calculated 3D binary-segmentation mask, the resulting porosity value, and other derived results (e.g., segmentation pipeline runtime). The analysis module presents the data at different levels of detail by drill-down filtering in order to determine accurate and robust segmentation pipelines. Overview visualizations allow to initially compare and evaluate the segmentation pipelines. With a scatter plot matrix (SPLOM), the segmentation pipelines are examined in more detail based on their input and output parameters. Individual segmentation-pipeline runs are selected in the SPLOM and visually examined and compared in 2D slice views and 3D renderings by using aggregated segmentation masks and statistical contour renderings. PorosityAnalyzer has been thoroughly evaluated with the help of twelve domain experts. Two case studies demonstrate the applicability of our proposed concepts and visualization techniques, and show that our tool helps domain experts to gain new insights and improve their workflow efficiency.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.