CobWeb 1.0: machine learning toolbox for tomographic imaging

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

doi:https://doi.org/10.5194/gmd-13-315-2020

Articles | Volume 13, issue 1

https://doi.org/10.5194/gmd-13-315-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-315-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 1

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

Supplement

https://doi.org/10.5194/gmd-13-315-2020-supplement

Data sets

Synchrotron tomography dataset of Gas Hydrate S. Chauhan, K. Sell, Kathleen, F. Enzmann, W. Rühaak, T. Wille, I. Sass, and M. Kersten https://doi.org/10.5281/zenodo.2390943

Model code and software

CobWeb 1.0: machine learning tool box for tomographic imaging S. Chauhan, K. Sell, Kathleen, W. Rühaak, T. Wille, I. Sass https://doi.org/10.5281/zenodo.2390943

Video supplement

CobWeb 1.0 Demo S. Chauhan, K. Sell, Kathleen, W. Rühaak, T. Wille, I. Sass https://doi.org/10.5281/zenodo.2390943

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.