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

Quintanilla Domínguez, Joel; Cortina Januchs, María Guadalupe; Ojeda Magaña, Benjamín; Vega Corona, Antonio; Tarquis Alfonso, Ana Maria y 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. En: "8th EGU General Assembly, EGU 2011", 03/04/2011 - 08/04/2011, Viena, Austria. p. 11829.

Descripción

Título: Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images
Autor/es:
  • Quintanilla Domínguez, Joel
  • Cortina Januchs, María Guadalupe
  • Ojeda Magaña, Benjamín
  • Vega Corona, Antonio
  • Tarquis Alfonso, Ana Maria
  • Andina de la Fuente, Diego
Tipo de Documento: Ponencia en Congreso o Jornada (Otro)
Título del Evento: 8th EGU General Assembly, EGU 2011
Fechas del Evento: 03/04/2011 - 08/04/2011
Lugar del Evento: Viena, Austria
Título del Libro: Geophysical Research Abstracts of 8th EGU General Assembly
Fecha: 2011
Volumen: 13
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 13265
Identificador DC: http://oa.upm.es/13265/
Identificador OAI: oai:oa.upm.es:13265
URL Oficial: http://www.geophysical-research-abstracts.net
Depositado por: Memoria Investigacion
Depositado el: 28 Nov 2012 11:05
Ultima Modificación: 21 Abr 2016 12:34
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