Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/80268
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dc.contributor.authorPrisacariu, V.en
dc.contributor.authorSegal, A.en
dc.contributor.authorReid, I.en
dc.date.issued2013en
dc.identifier.citation11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012 / K. M. Lee, Y. Matsushita, J. M. Rehg, Z. Hu (eds.):593-606en
dc.identifier.isbn9783642373305en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.urihttp://hdl.handle.net/2440/80268-
dc.description.abstractWe propose a novel framework for joint 2D segmentation and 3D pose and 3D shape recovery, for images coming from a single monocular source. In the past, integration of all three has proven difficult, largely because of the high degree of ambiguity in the 2D - 3D mapping. Our solution is to learn nonlinear and probabilistic low dimensional latent spaces, using the Gaussian Process Latent Variable Models dimensionality reduction technique. These act as class or activity constraints to a simultaneous and variational segmentation – recovery – reconstruction process. We define an image and level set based energy function, which we minimise with respect to 3D pose and shape, 2D segmentation resulting automatically as the projection of the recovered shape under the recovered pose. We represent 3D shapes as zero levels of 3D level set embedding functions, which we project down directly to probabilistic 2D occupancy maps, without the requirement of an intermediary explicit contour stage. Finally, we detail a fast, open-source, GPU-based implementation of our algorithm, which we use to produce results on both real and artificial video sequences.en
dc.description.statementofresponsibilityVictor Adrian Prisacariu, Aleksandr V. Segal, and Ian Reiden
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.rights© Springer-Verlag Berlin Heidelberg 2013en
dc.titleSimultaneous monocular 2D segmentation, 3D pose recovery and 3D reconstructionen
dc.typeConference paperen
dc.identifier.rmid0020131113en
dc.contributor.conferenceComputer Vision (2012 : Daejeon, Korea)en
dc.identifier.doi10.1007/978-3-642-37331-2_45en
dc.publisher.placeBerlin Heidelbergen
dc.identifier.pubid18447-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidReid, I. [0000-0001-7790-6423]en
Appears in Collections:Computer Science publications

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