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| Titel |
Detection of pore space in CT soil images using artificial neural networks |
| VerfasserIn |
M. G. Cortina-Januchs, J. Quintanilla-Dominguez, A. Vega-Corona, A. M. Tarquis, D. Andina |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 8, no. 2 ; Nr. 8, no. 2 (2011-02-09), S.279-288 |
| Datensatznummer |
250005441
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| Publikation (Nr.) |
 copernicus.org/bg-8-279-2011.pdf |
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| Zusammenfassung |
| Computed Tomography (CT) images provide a non-invasive alternative for
observing soil structures, particularly pore space. Pore space in soil data
indicates empty or free space in the sense that no material is present there
except fluids such as air, water, and gas. Fluid transport depends on where
pore spaces are located in the soil, and for this reason, it is important to
identify pore zones. The low contrast between soil and pore space in CT
images presents a problem with respect to pore quantification. In this
paper, we present a methodology that integrates image processing, clustering
techniques and artificial neural networks, in order to classify pore space
in soil images. Image processing was used for the feature extraction of
images. Three clustering algorithms were implemented (K-means, Fuzzy
C-means, and Self Organising Maps) to segment images. The objective of
clustering process is to find pixel groups of a similar grey level intensity
and to organise them into more or less homogeneous groups. The segmented
images are used for test a classifier. An Artificial Neural Network is
characterised by a great degree of modularity and flexibility, and it is
very efficient for large-scale and generic pattern recognition applications.
For these reasons, an Artificial Neural Network was used to classify soil
images into two classes (pore space and solid soil). Our methodology shows
an alternative way to detect solid soil and pore space in CT images. The
percentages of correct classifications of pore space of the total number of
classifications among the tested images were 97.01%, 96.47% and
96.12%. |
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