Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

Quintanilla Domínguez, Joel and Cortina Januchs, María Guadalupe and Ojeda Magaña, Benjamín and Vega Corona, Antonio and Tarquis Alfonso, Ana Maria and Andina de la Fuente, Diego (2011). Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images. In: "8th EGU General Assembly, EGU 2011", 03/04/2011 - 08/04/2011, Viena, Austria. p. 11829.

Description

Title: Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images
Author/s:
  • Quintanilla Domínguez, Joel
  • Cortina Januchs, María Guadalupe
  • Ojeda Magaña, Benjamín
  • Vega Corona, Antonio
  • Tarquis Alfonso, Ana Maria
  • Andina de la Fuente, Diego
Item Type: Presentation at Congress or Conference (Other)
Event Title: 8th EGU General Assembly, EGU 2011
Event Dates: 03/04/2011 - 08/04/2011
Event Location: Viena, Austria
Title of Book: Geophysical Research Abstracts of 8th EGU General Assembly
Date: 2011
Volume: 13
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

More information

Item ID: 13265
DC Identifier: http://oa.upm.es/13265/
OAI Identifier: oai:oa.upm.es:13265
Official URL: http://www.geophysical-research-abstracts.net
Deposited by: Memoria Investigacion
Deposited on: 28 Nov 2012 11:05
Last Modified: 21 Apr 2016 12:34
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