Scanning-Electron images from North Sea Chalk are studied for important rock properties. To relieve this manual labor, we investigated several standard image processing methods that underperformed on complicated chalk. Due to the lack of manually labeled data, deep neural networks could not be adequately applied. Gaussian Mixture Models learnt a two-fold representation that separated the background well from the rock. Subsequent morphological filtering cleans up the prediction and enables automatic analysis.


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  1. Lindsay, B. G.
    (1995, January). Mixture models: theory, geometry and applications. In NSF-CBMS regional conference series in probability and statistics (pp. i–163). Institute of Mathematical Statistics and the American Statistical Association.
    [Google Scholar]
  2. Modarres, M. H., Aversa, R., Cozzini, S., Ciancio, R., Leto, A., & Brandino, G. P.
    (2017). Neural Network for Nanoscience Scanning Electron Microscope Image Recognition. Scientific reports, 7 (1), 13282.
    [Google Scholar]
  3. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J.
    (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12 (Oct), 2825–2830.
    [Google Scholar]
  4. Ronneberger, O., Fischer, P., & Brox, T.
    (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234–241). Springer, Cham.
    [Google Scholar]
  5. Serra, J., & Vincent, L.
    (1992). An overview of morphological filtering. Circuits, Systems and Signal Processing, 11 (1), 47–108.
    [Google Scholar]

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