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AuthorFurat, Orkundc.contributor.author
AuthorWang, Mingyandc.contributor.author
AuthorNeumann, Matthiasdc.contributor.author
AuthorPetrich, Lukasdc.contributor.author
AuthorWeber, Matthiasdc.contributor.author
AuthorKrill, III, Carl E.dc.contributor.author
AuthorSchmidt, Volkerdc.contributor.author
Date of accession2019-09-25T12:57:09Zdc.date.accessioned
Available in OPARU since2019-09-25T12:57:09Zdc.date.available
Date of first publication2019dc.date.issued
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
TitleMachine Learning Techniques for the Segmentation of Tomographic Image Data of Functional Materialsdc.title
Resource typeWissenschaftlicher Artikeldc.type
FacultyFakultät für Mathematik und Wirtschaftswissenschaftenuulm.affiliationGeneral
InstitutionInstitut für Stochastikuulm.affiliationSpecific
InstitutionInstitut für Funktionelle Nanosystemeuulm.affiliationSpecific
DCMI TypeTextuulm.typeDCMI
CategoryPublikationsnachweiseuulm.category
DOI (external)10.3389/fmats.2019.00145dc.identifier.doiExternal
Source - Title of sourceFrontiers in Materialssource.title
Source - Place of publicationFrontiers Media SAsource.publisher
Source - Volume6source.volume
Source - Year2019source.year
Source - Article number145source.articleNumber
Source - ISSN2296-8016source.identifier.issn
FundingDeutsche Forschungsgemeinschaft (DFG) [SCHM997/23-1]uulm.funding
Open AccessDOAJ Golduulm.OA
Suitable communityFakultät für Mathematik und Wirtschaftswissenschaftenuulm.community
Suitable communityFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.community
WoS000473267500002uulm.identifier.wos
University Bibliographyjauulm.unibibliographie


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