Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-315-2020
https://doi.org/10.5194/gmd-13-315-2020
Model description paper
 | 
31 Jan 2020
Model description paper |  | 31 Jan 2020

CobWeb 1.0: machine learning toolbox for tomographic imaging

Swarup Chauhan, Kathleen Sell, Wolfram Rühaak, Thorsten Wille, and Ingo Sass

Viewed

Total article views: 7,367 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,697 1,528 142 7,367 385 133 211
  • HTML: 5,697
  • PDF: 1,528
  • XML: 142
  • Total: 7,367
  • Supplement: 385
  • BibTeX: 133
  • EndNote: 211
Views and downloads (calculated since 20 Mar 2019)
Cumulative views and downloads (calculated since 20 Mar 2019)

Viewed (geographical distribution)

Total article views: 7,367 (including HTML, PDF, and XML) Thereof 6,789 with geography defined and 578 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Apr 2026
Download
Short summary
We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity and volume fraction. We demonstrate a novel approach of dual filtration and dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.
Share