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dc.contributor.advisorHeidrich, Wolfgang
dc.contributor.authorZang, Guangming
dc.date.accessioned2020-02-09T05:37:48Z
dc.date.available2020-02-09T05:37:48Z
dc.date.issued2020-02-06
dc.identifier.citationZang, G. (2020). Space-Time Tomographic Reconstruction of Deforming Objects. KAUST Research Repository. https://doi.org/10.25781/KAUST-1W66Y
dc.identifier.doi10.25781/KAUST-1W66Y
dc.identifier.urihttp://hdl.handle.net/10754/661420
dc.description.abstractX-ray computed tomography (CT) is a popular imaging technique used for reconstructing volumetric properties for a large range of objects. Compared to traditional optical means, CT is a valuable tool for analyzing objects with interesting internal structure or complex geometries that are not accessible with. In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. The second application is about space-time tomography: Through a combination of a new CT image acquisition strategy, a space-time tomographic image formation model, and an alternating, multi-scale solver, we achieve a general approach that can be used to analyze a wide range of dynamic phenomena. Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging (e.g., 4D soot imaging of a combustion process, a mixing fluid process, a fuel injection process, and view synthesis for visible light tomography), which further demonstrates the flexibility of our optimization framework.
dc.language.isoen
dc.subjecttomography
dc.subjectX-ray
dc.subjectreconstruction
dc.subjectcomputational imaging
dc.subjectproximal algorithms
dc.subjectmotion capture
dc.titleSpace-Time Tomographic Reconstruction of Deforming Objects
dc.typeDissertation
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberWonka, Peter
dc.contributor.committeememberLubineau, Gilles
dc.contributor.committeememberDe Carlo, Francesco
thesis.degree.disciplineComputer Science
thesis.degree.nameDoctor of Philosophy
refterms.dateFOA2020-02-09T05:37:48Z
kaust.request.doiyes


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