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Gaussian Mixture Models For Robust Unsupervised Scanning-Electron Microscopy Image Segmentation Of North Sea ChalkNormal access

Authors: J.S. Dramsch, F. Amour and M. Lüthje
Event name: First EAGE/PESGB Workshop Machine Learning
Session: Case Studies II
Publication date: 30 November 2018
DOI: 10.3997/2214-4609.201803014
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 819.7Kb )
Price: € 20

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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