Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Visualization Group
  • VRVis Competence Center
• Date: February 2019
• Booktitle: International Conference on Industrial Computed Tomography (ICT) 2019
• Call for Papers: Call for Paper
• Date (from): 13. February 2019
• Date (to): 15. February 2019
• Editor: Simone Carmignato
• Event: International Conference on Industrial Computed Tomography (ICT) 2019
• Lecturer: Harald Steinlechner
• Location: Padova, Italy
• Pages (from): 1
• Pages (to): 6
• Keywords: CT, GPU, Inclusion Detection, Interactive Visualisation, VisualAnalysis, Parallel Coordinates, Volume Rendering

Abstract

CT data of industrially produced cast metal parts are often afflicted with artefacts due to complex geometries ill-suited for the scanning process. Simple global threshold-based porosity detection algorithms usually fail to deliver meaningful results. Other adaptive methods can handle image artefacts, but require long preprocessing times. This makes an efficient analysis workflow infeasible. We propose an alternative approach for analyzing and visualizing volume defects in a fully interactive manner, where analyzing volumes becomes more of an interactive exploration instead of time-consuming parameter guessing interrupted by long processing times. Our system is based on a highly efficient GPU implementation of a segmentation algorithm for porosity detection. The runtime is on the order of seconds for a full volume and parametrization is kept simple due to a single threshold parameter. A fully interactive user interface comprised of multiple linked views allows to quickly identify defects of interest, while filtering out artefacts even in noisy areas.

Additional Files and Images

Weblinks

• Entry in the publication database of TU-Wien

BibTeX

@inproceedings{STEINLECHNER-2019-ICT,
  title =      "A Novel Approach for Immediate, Interactive CT Data
               Visualization andEvaluation using GPU-based Segmentation and
               Visual Analysis",
  author =     "Harald Steinlechner and Georg Haaser and Bernd Oberdorfer
               and Daniel Habe and Stefan Maierhofer and Michael
               Schw\"{a}rzler and Meister Eduard Gr\"{o}ller",
  year =       "2019",
  abstract =   "CT data of industrially produced cast metal parts are often
               afflicted with artefacts due to complex geometries
               ill-suited for the scanning process. Simple global
               threshold-based porosity detection algorithms usually fail
               to deliver meaningful results. Other adaptive methods can
               handle image artefacts, but require long preprocessing
               times. This makes an efficient analysis workflow infeasible.
               We propose an alternative approach for analyzing and
               visualizing volume defects in a fully interactive manner,
               where analyzing volumes becomes more of an interactive
               exploration instead of time-consuming parameter guessing
               interrupted by long processing times. Our system is based on
               a highly efficient GPU implementation of a segmentation
               algorithm for porosity detection. The runtime is on the
               order of seconds for a full volume and parametrization is
               kept simple due to a single threshold parameter. A fully
               interactive user interface comprised of multiple linked
               views allows to quickly identify defects of interest, while
               filtering out artefacts even in noisy areas.",
  month =      feb,
  booktitle =  "International Conference on Industrial Computed Tomography
               (ICT) 2019",
  editor =     "Simone Carmignato",
  event =      "International Conference on Industrial Computed Tomography
               (ICT) 2019",
  location =   "Padova, Italy",
  pages =      "1--6",
  keywords =   "CT, GPU, Inclusion Detection, Interactive Visualisation,
               VisualAnalysis, Parallel Coordinates, Volume Rendering",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/STEINLECHNER-2019-ICT/",
}