Riemannian level-set methods for tensor-valued data

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dc.creatorZerai, Mourad
dc.creatorMoakher, Maher
dc.date2007-05-02
dc.date.accessioned2026-07-07T07:59:06Z
dc.date.available2026-07-07T07:59:06Z
dc.descriptionWe present a novel approach for the derivation of PDE modeling curvature-driven flows for matrix-valued data. This approach is based on the Riemannian geometry of the manifold of Symmetric Positive Definite Matrices Pos(n).
dc.description11 pages, 03 figures, to be published in the proceedings of SSVM 2007, LNCS Springer
dc.identifierhttps://arxiv.org/abs/0705.0214
dc.identifierhttp://arxiv.org/abs/0705.0214
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128246
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.4.3
dc.titleRiemannian level-set methods for tensor-valued data
dc.typetext

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