Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
- Publication type:
- Journal article
- Metadata:
-
- Autoren
- Faisal Khan
- Frieder Enzmann
- Michael Kersten
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000374543400012&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.5194/se-7-481-2016
- eISSN
- 1869-9529
- Externe Identifier
- Clarivate Analytics Document Solution ID: DJ9OS
- ISSN
- 1869-9510
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- SOLID EARTH
- Paginierung
- 481 - 492
- Datum der Veröffentlichung
- 2016
- Status
- Published
- Titel
- Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
- Sub types
- Article
- Ausgabe der Zeitschrift
- 7
Data source: Web of Science (Lite)
- Other metadata sources:
-
- Abstract
- <jats:p>Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples. </jats:p>
- Autoren
- Faisal Khan
- Frieder Enzmann
- Michael Kersten
- DOI
- 10.5194/se-7-481-2016
- eISSN
- 1869-9529
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- Solid Earth
- Sprache
- en
- Online publication date
- 2016
- Paginierung
- 481 - 492
- Status
- Published online
- Herausgeber
- Copernicus GmbH
- Herausgeber URL
- http://dx.doi.org/10.5194/se-7-481-2016
- Datum der Datenerfassung
- 2021
- Titel
- Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
- Ausgabe der Zeitschrift
- 7
Data source: Crossref
- Author's licence
- CC-BY
- Autoren
- Faisal Khan
- Frieder Enzmann
- Michael Kersten
- Hosting institution
- Universitätsbibliothek Mainz
- Sammlungen
- JGU-Publikationen
- Resource version
- Published version
- URN
- urn:nbn:de:hebis:77-publ-545088
- DOI
- 10.5194/se-7-481-2016
- File(s) embargoed
- false
- Open access
- true
- ISSN
- 1869-9529
- Ausgabe der Veröffentlichung
- 2
- Zeitschrift
- Solid earth
- Schlüsselwörter
- 550 Geowissenschaften
- 550 Earth sciences
- Sprache
- eng
- Open access status
- Open Access
- Paginierung
- 481 - 492
- Datum der Veröffentlichung
- 2016
- Public URL
- https://openscience.ub.uni-mainz.de/handle/20.500.12030/689
- Herausgeber
- Copernicus Publ.
- Herausgeber URL
- http://dx.doi.org/10.5194/se-7-481-2016
- Datum der Datenerfassung
- 2016
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2016
- Zugang
- Public
- Titel
- Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
- Ausgabe der Zeitschrift
- 7
Files
54508.pdf
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