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dc.contributor.authorWu, Bin
dc.contributor.authorRahman, Talal
dc.contributor.authorTai, Xue-Cheng
dc.date.accessioned2021-03-22T08:44:32Z
dc.date.available2021-03-22T08:44:32Z
dc.date.created2019-01-27T16:15:07Z
dc.date.issued2018
dc.identifier.citationWu, B., Rahman, T., & Tai, X.-C. (2018). Sparse-data based 3d surface reconstruction for cartoon and map. In X.-C. Tai, E. Bae, & M. Lysaker (Eds.), Imaging, vision and learning based on optimization and pdes (pp. 47-64). Springer.en_US
dc.identifier.isbn978-3-319-91273-8
dc.identifier.urihttps://hdl.handle.net/11250/2734672
dc.description.abstractA model combining the first-order and the second-order variational regularizations for the purpose of 3D surface reconstruction based on 2D sparse data is proposed. The model includes a hybrid fidelity constraint which allows the initial conditions to be switched flexibly between vectors and elevations. A numerical algorithm based on the augmented Lagrangian method is also proposed. The numerical experiments are presented, showing its excellent performance both in designing cartoon characters, as well as in recovering oriented three dimensional maps from contours or points with elevation information.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofImaging, Vision and Learning Based on Optimization and PDEs
dc.titleSparse-Data Based 3D Surface Reconstruction for Cartoon and Mapen_US
dc.typeChapteren_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderThis is an author's accepted manuscript (postprint) of a chapter published by Springer in the Mathematics and Visualization series on 20 November 2018. The final, authenticated version is available at https://doi.org/10.1007/978-3-319-91274-5_3en_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429en_US
dc.source.pagenumber47-64en_US
dc.identifier.doi10.1007/978-3-319-91274-5_3
dc.identifier.cristin1665697
dc.relation.projectNorges forskningsråd: 239033en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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