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Deep learning-based 2D-3D sample pose estimation for X-ray 3DCT

dc.contributor.authorPresenti, Alice
dc.contributor.authorBazrafkan, Shabab
dc.contributor.authorSijbers, Jan
dc.contributor.authorDe Beenhouwer, Jan
dc.date.accessioned2021-10-29T02:24:14Z
dc.date.available2021-10-29T02:24:14Z
dc.date.issued2020-02
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/35763
dc.sourceIIOimport
dc.titleDeep learning-based 2D-3D sample pose estimation for X-ray 3DCT
dc.typeProceedings paper
dc.contributor.imecauthorPresenti, Alice
dc.contributor.imecauthorBazrafkan, Shabab
dc.contributor.imecauthorSijbers, Jan
dc.contributor.imecauthorDe Beenhouwer, Jan
dc.contributor.orcidimecSijbers, Jan::0000-0003-4225-2487
dc.contributor.orcidimecDe Beenhouwer, Jan::0000-0001-5253-1274
dc.date.embargo9999-12-31
dc.source.peerreviewyes
dc.source.beginpage1
dc.source.endpage6
dc.source.conference10th Conference on Industrial Computed Tomography (ICT 2020)
dc.source.conferencedate4/02/2020
dc.source.conferencelocationWels Austria
dc.identifier.urlhttps://www.ndt.net/article/ctc2020/papers/ICT2020_paper_id165.pdf
imec.availabilityPublished - open access


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