<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Variational Methods for Discrete Tomography"^^ . "Image reconstruction from tomographic sampled data has contoured as a stand alone research area with application in many practical situations, in domains\r\nsuch as medical imaging, seismology, astronomy, flow analysis, industrial inspection and many more. Already existing algorithms on the market (continuous)\r\nfail in being able to model the analysed object. In this thesis, we study discrete tomographic approaches that enable the addition of constraints in order to better\r\nfit the description of the analysed object and improve the end result. A particular focus is set on assumptions regarding the signals' sampling methodology, point\r\nat which we look towards the recently introduced Compressive Sensing (CS) approach, that has shown to return remarkable results based on how sparse a given\r\nsignal is. However, research done in the CS field does not accurately relate to real world applications, as objects usually surrounding us are considered to be piece-wise constant (not sparse on their own) and the properties of the sensing matrices from the viewpoint of CS do not re\r\nect real acquisition processes. Motivated by these shortcomings, we study signals that are sparse in a given representation, e.g. the forward-difference operator (total variation) and develop reconstruction diagrams (phase transitions) with the help of linear programming, convex analysis and duality that enable the user to pin-point the type of objects (with regard to their sparsity) which can be reconstructed, given an ensemble of acquisition\r\ndirections. Moreover, a closer look is given to handling large data volumes, by adding different perturbations (entropic, quadratic) to the already constrained\r\nlinear program. In empirical assessments, perturbation has lead to an increased reconstruction rate. Needless to say, the topic of this thesis is motivated by industrial applications where the acquisition process is restricted to a maximum of nine cameras, thus returning a severely undersampled inverse problem."^^ . "2016" . . . . . . . "Andreea-Marieta"^^ . "Denitiu"^^ . "Andreea-Marieta Denitiu"^^ . . . . . . "Variational Methods for Discrete Tomography (PDF)"^^ . . . "Denitiu_Dissertation.pdf"^^ . . . "Variational Methods for Discrete Tomography (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Variational Methods for Discrete Tomography (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Variational Methods for Discrete Tomography (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Variational Methods for Discrete Tomography (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Variational Methods for Discrete Tomography (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #21683 \n\nVariational Methods for Discrete Tomography\n\n" . "text/html" . . . "004 Informatik"@de . "004 Data processing Computer science"@en . .