journal article·2020-07-02Open access

Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres

Bernhard Fröhler, Tim Elberfeld, Torsten Möller, Hans-Christian Hege, Jan De Beenhouwer, Jan Sijbers, Johann Kastner, Christoph Heinzl

Abstract

We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Computer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Digital Image Processing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Research Group Computed Tomography, University of Applied Sciences Upper Austria, Wels, Austria

Data Science @ Uni Vienna, and Faculty of Computer Science, University of Vienna, Vienna, Austria;

Imec-Vision Lab, University of Antwerp, Antwerp, Belgium

Research Group Visual Data Analysis, Zuse Institute Berlin, Berlin, Germany

Funding

FWO · Austrian Science Fund · PSSP · FFG

References (20)

Show source-provided references
1. Plank B · 2019
2. 10.1063/1.511792810.1063/1.5117928 ↗
3. 10.1007/978-3-319-30050-4_5-110.1007/978-3-319-30050-4_5-1 ↗
4. 10.1016/j.ndteint.2018.01.00110.1016/j.ndteint.2018.01.001 ↗
5. Salaberger D, Kannappan KA, Kastner J, et al. Evaluation of computed tomography data from fibre reinforced polymers to determine fibre length distribution. Int Polym Proc. 2011;26(3):283–291.10.3139/217.2441 ↗
6. 10.4028/www.scientific.net/MSF.825-826.90710.4028/www.scientific.net/MSF.825-826.907 ↗
7. Kolling RGS · Comput Eng · 2016 · 2016
8. Konopczyński T, Rathore D, Rathore J, et al. Fully convolutional deep network architectures for automatic short glass fiber semantic segmentation from CT scans. In: Conf. Industrial Computed Tomography; 2018. Available from: https://ndt.net/?id=21916.
9. 10.1007/s10921-018-0514-010.1007/s10921-018-0514-0 ↗
10. Emerson MJ · 2019
11. Weissenböck J · 2014 · 153
12. 10.1111/cgf.1368810.1111/cgf.13688 ↗
13. Salaberger D Micro-structure of discontinuous fibre polymer matrix composites determined by X-ray computed tomography [dissertation]. TU Wien; 2019. Available from: http://repositum.tuwien.ac.at/obvutwhs/content/titleinfo/3581550.
14. 10.3389/fmats.2019.0022610.3389/fmats.2019.00226 ↗
15. Zhang H · Compos Part B Eng · 2018 · 2 · 151
16. Senck S · 2019
17. 10.1111/cgf.1321410.1111/cgf.13214 ↗
18. 10.1007/978-3-642-27343-8_1010.1007/978-3-642-27343-8_10 ↗
19. 10.21105/joss.0118510.21105/joss.01185 ↗
20. 10.1117/12.253483610.1117/12.2534836 ↗