Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk

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Abstract

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.
Original languageEnglish
Publication date2018
Number of pages2
DOIs
Publication statusPublished - 2018
EventFirst EAGE/PESGB Workshop Machine Learning - Olympia London, London, United Kingdom
Duration: 29 Nov 201830 Nov 2018

Conference

ConferenceFirst EAGE/PESGB Workshop Machine Learning
LocationOlympia London
CountryUnited Kingdom
CityLondon
Period29/11/201830/11/2018

Cite this

@conference{4108cdc5f75e4353bc2f00c9521aee38,
title = "Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk",
abstract = "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.",
author = "{S{\"o}ren Dramsch}, Jesper and Fr{\'e}d{\'e}ric Amour and Mikael L{\"u}thje",
year = "2018",
doi = "10.3997/2214-4609.201803014",
language = "English",
note = "First EAGE/PESGB Workshop Machine Learning ; Conference date: 29-11-2018 Through 30-11-2018",

}

Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk. / Sören Dramsch, Jesper; Amour, Frédéric; Lüthje, Mikael.

2018. Paper presented at First EAGE/PESGB Workshop Machine Learning , London, United Kingdom.

Research output: Contribution to conferencePaperResearchpeer-review

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T1 - Gaussian Mixture Models for Robust Unsupervised Scanning-Electron Microscopy Image Segmentation of North Sea Chalk

AU - Sören Dramsch, Jesper

AU - Amour, Frédéric

AU - Lüthje, Mikael

PY - 2018

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AB - 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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