Assessment of segmentation methods for pore detection in cellular concrete images

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dc.contributor.affiliationGaviria-Hdz, J.F., Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, Colombia
dc.contributor.affiliationMedina, L.J., Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, Colombia
dc.contributor.affiliationMera, C., Facultad de Ingeniería, Instituto Tecnológico Metropolitano, Medellín, Colombia
dc.contributor.affiliationChica, L., Facultad de Ingenierías, Universidad de Medellín, Medellín, Colombia
dc.contributor.affiliationSepúlveda-Cano, L.M., Facultad de Ingenierías, Universidad de Medellín, Medellín, Colombia
dc.contributor.authorGaviria-Hdz J.F.
dc.contributor.authorMedina L.J.
dc.contributor.authorMera C.
dc.contributor.authorChica L.
dc.contributor.authorSepúlveda-Cano L.M.
dc.date2019
dc.date.accessioned2021-02-05T14:59:02Z
dc.date.available2021-02-05T14:59:02Z
dc.descriptionIn the last years the use of cellular concretes has been extended due to the rise in the ratio strength/weight reached. Porosity is a property that must be taken into account because it is associated directly to the performance of a cellular concrete. The mercury porosimetry and vacuum saturation are test used to concrete porosity. However, these tests are expensive, and it requires a careful preparation of samples. Another way to determine porosity and pore distribution over concrete is reconstruction using high-resolution images from microscopy. As an alternative, in this work we compare traditional edge detection methods and fractional derivate method to detect the pores in images taken from a flat sample of cellular concrete. The experiments show that the method based on fractional derivate is more accurate to detect the pores, which is the first step to estimate total porosity of cellular concrete through non-specialized images. © 2019 IEEE.
dc.identifier.doi10.1109/STSIVA.2019.8730220
dc.identifier.isbn9781728114910
dc.identifier.urihttp://hdl.handle.net/11407/6060
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.spa
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068066499&doi=10.1109%2fSTSIVA.2019.8730220&partnerID=40&md5=c28abd489046fd91f7f9f191f9ec7e5a
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dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.source2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings
dc.subjectCellular concretespa
dc.subjectFractional Derivativespa
dc.subjectPore segmentationspa
dc.subjectPorosity estimationspa
dc.subject.proposalConcreteseng
dc.subject.proposalEdge detectioneng
dc.subject.proposalPorosityeng
dc.subject.proposalVisioneng
dc.subject.proposalCellular concreteseng
dc.subject.proposalEdge detection methodseng
dc.subject.proposalFractional derivativeseng
dc.subject.proposalHigh resolution imageeng
dc.subject.proposalMercury porosimetryeng
dc.subject.proposalPorosity estimationeng
dc.subject.proposalSegmentation methodseng
dc.subject.proposalVacuum saturationeng
dc.subject.proposalImage segmentationeng
dc.titleAssessment of segmentation methods for pore detection in cellular concrete images
dc.typeConference Paper
dc.type.driverinfo:eu-repo/semantics/other
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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