Segmentation of sandstone thin section images with separation of touching grains using optimum path forest operators

dc.creatorMingireanov, I
dc.creatorSpina, TV
dc.creatorFalcao, AX
dc.creatorVidal, AC
dc.date2013
dc.dateAUG
dc.date2014-07-30T18:31:40Z
dc.date2015-11-26T16:53:53Z
dc.date2014-07-30T18:31:40Z
dc.date2015-11-26T16:53:53Z
dc.date.accessioned2018-03-28T23:41:06Z
dc.date.available2018-03-28T23:41:06Z
dc.identifierComputers & Geosciences. Pergamon-elsevier Science Ltd, v. 57, n. 146, n. 157, 2013.
dc.identifier0098-3004
dc.identifierWOS:000320825400016
dc.identifier10.1016/j.cageo.2013.04.011
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/71324
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/71324
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1276700
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThe segmentation of detrical sedimentary rock images is still a challenge for characterization of grain morphology in sedimentary petrography. We propose a fast and effective approach that first segments the grains from pore in sandstone thin section images and separates the touching grains automatically, and second lets the user to correct the misclassified grains with minimum interaction. The method is mostly based on the image foresting transform (IFT)-a tool for the design of image processing operators using optimum connectivity. The IFT interprets an image as a graph, whose nodes are the image pixels, the arcs are defined by an adjacency relation between pixels, and the paths are valued by a connectivity function. The IFT algorithm transforms the image graph into an optimum-path forest and distinct operators are designed by suitable choice of the IFT parameters and post-processing of the attributes of that forest. The solution involves a sequence of three IFT-based image operators for automatic segmentation and the interactive segmentation combines region- and boundary-based object delineation using two IFT operators. Tests with thin section images of two different sandstone samples have shown very satisfactory results, yielding r(2) and accuracy parameters of 0.8712 and 94.8% on average, respectively. Biases were the presence of the matrix and rock fragments. (C) 2013 Elsevier Ltd. All rights reserved.
dc.description57
dc.description146
dc.description157
dc.descriptionPRH/ANP [2010/3360-9]
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionPRH/ANP [2010/3360-9]
dc.descriptionCNPq [303673/2010-9]
dc.descriptionFAPESP [2007/52015-0, 2011/01434-9]
dc.languageen
dc.publisherPergamon-elsevier Science Ltd
dc.publisherOxford
dc.publisherInglaterra
dc.relationComputers & Geosciences
dc.relationComput. Geosci.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectSandstone thin section image analysis
dc.subjectImage foresting transform
dc.subjectAutomatic image segmentation by optimum-path forest
dc.subjectAnd interactive segmentation by live markers
dc.subjectBoundary Detection
dc.subjectLive Wire
dc.titleSegmentation of sandstone thin section images with separation of touching grains using optimum path forest operators
dc.typeArtículos de revistas


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