Measuring statistical geometric properties of tomographic images of soils
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/27612 |
Resumo: | Porous media modeling is relevant in several applications, such as agricultural engineering, where soil compaction analysis requires the estimation of soil transport properties. For example, the prediction of root growing patterns and their environmental impact is usually measured by analyzing soil fluid infiltration capacity and water retention. Recently, tomographic images have been used in nondestructive tests of soil. However, using such images is challenging for two reasons: 1) Tomographic images are usually noisy, which complicates their segmentation, and 2) modeling the soil structure requires establishing adjacency relations among neighboring tomographic slices, which has a significant computational cost due to the combinatorial nature of this problem. In this paper, we propose a solution for both problems. The experimental results show that soil samples can be analyzed and classified with significant accuracy using our proposed approach. |
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Felipussi, Siovani CintraScharcanski, JacobComba, Joao Luiz Dihl2011-01-29T06:00:40Z20080018-9456http://hdl.handle.net/10183/27612000665884Porous media modeling is relevant in several applications, such as agricultural engineering, where soil compaction analysis requires the estimation of soil transport properties. For example, the prediction of root growing patterns and their environmental impact is usually measured by analyzing soil fluid infiltration capacity and water retention. Recently, tomographic images have been used in nondestructive tests of soil. However, using such images is challenging for two reasons: 1) Tomographic images are usually noisy, which complicates their segmentation, and 2) modeling the soil structure requires establishing adjacency relations among neighboring tomographic slices, which has a significant computational cost due to the combinatorial nature of this problem. In this paper, we propose a solution for both problems. The experimental results show that soil samples can be analyzed and classified with significant accuracy using our proposed approach.application/pdfengIEEE transactions on instrumentation and measurement. New York. Vol. 57, no 11 (Nov. 2008), p. 2502-2512Computação gráficaProcessamento de imagensAdaptive systemsFeature extractionGeometric modelingImage processingMaterials testingPorous mediaSoil measurementsStatistical geometryStatisticsMeasuring statistical geometric properties of tomographic images of soilsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000665884.pdf.txt000665884.pdf.txtExtracted Texttext/plain47505http://www.lume.ufrgs.br/bitstream/10183/27612/2/000665884.pdf.txtc213dc0f6843f90737943185c1861bb4MD52ORIGINAL000665884.pdf000665884.pdfTexto completo (inglês)application/pdf1164400http://www.lume.ufrgs.br/bitstream/10183/27612/1/000665884.pdf050ad860dd5809f0f7af4438f490efc2MD51THUMBNAIL000665884.pdf.jpg000665884.pdf.jpgGenerated Thumbnailimage/jpeg2196http://www.lume.ufrgs.br/bitstream/10183/27612/3/000665884.pdf.jpg39e05617460ce0f19ce4ce2cc5c5e4dfMD5310183/276122021-06-13 04:31:47.792964oai:www.lume.ufrgs.br:10183/27612Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-06-13T07:31:47Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Measuring statistical geometric properties of tomographic images of soils |
title |
Measuring statistical geometric properties of tomographic images of soils |
spellingShingle |
Measuring statistical geometric properties of tomographic images of soils Felipussi, Siovani Cintra Computação gráfica Processamento de imagens Adaptive systems Feature extraction Geometric modeling Image processing Materials testing Porous media Soil measurements Statistical geometry Statistics |
title_short |
Measuring statistical geometric properties of tomographic images of soils |
title_full |
Measuring statistical geometric properties of tomographic images of soils |
title_fullStr |
Measuring statistical geometric properties of tomographic images of soils |
title_full_unstemmed |
Measuring statistical geometric properties of tomographic images of soils |
title_sort |
Measuring statistical geometric properties of tomographic images of soils |
author |
Felipussi, Siovani Cintra |
author_facet |
Felipussi, Siovani Cintra Scharcanski, Jacob Comba, Joao Luiz Dihl |
author_role |
author |
author2 |
Scharcanski, Jacob Comba, Joao Luiz Dihl |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Felipussi, Siovani Cintra Scharcanski, Jacob Comba, Joao Luiz Dihl |
dc.subject.por.fl_str_mv |
Computação gráfica Processamento de imagens |
topic |
Computação gráfica Processamento de imagens Adaptive systems Feature extraction Geometric modeling Image processing Materials testing Porous media Soil measurements Statistical geometry Statistics |
dc.subject.eng.fl_str_mv |
Adaptive systems Feature extraction Geometric modeling Image processing Materials testing Porous media Soil measurements Statistical geometry Statistics |
description |
Porous media modeling is relevant in several applications, such as agricultural engineering, where soil compaction analysis requires the estimation of soil transport properties. For example, the prediction of root growing patterns and their environmental impact is usually measured by analyzing soil fluid infiltration capacity and water retention. Recently, tomographic images have been used in nondestructive tests of soil. However, using such images is challenging for two reasons: 1) Tomographic images are usually noisy, which complicates their segmentation, and 2) modeling the soil structure requires establishing adjacency relations among neighboring tomographic slices, which has a significant computational cost due to the combinatorial nature of this problem. In this paper, we propose a solution for both problems. The experimental results show that soil samples can be analyzed and classified with significant accuracy using our proposed approach. |
publishDate |
2008 |
dc.date.issued.fl_str_mv |
2008 |
dc.date.accessioned.fl_str_mv |
2011-01-29T06:00:40Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/27612 |
dc.identifier.issn.pt_BR.fl_str_mv |
0018-9456 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000665884 |
identifier_str_mv |
0018-9456 000665884 |
url |
http://hdl.handle.net/10183/27612 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
IEEE transactions on instrumentation and measurement. New York. Vol. 57, no 11 (Nov. 2008), p. 2502-2512 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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