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portada Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics: Dissertation
Type
Physical Book
Publisher
Author
Language
English
Pages
198
Format
Paperback
ISBN13
9783735742995

Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics: Dissertation

Stephan Wenger (Author) · Books On Demand · Paperback

Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics: Dissertation - Stephan Wenger

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Synopsis "Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics: Dissertation"

The field of computer graphics deals with virtual representations of the real world. These can be obtained either through reconstruction of a model from measurements, or by directly modeling a virtual object, often on a real-world example. The former is often formalized as a regularized optimization problem, in which a data term ensures consistency between model and data and a regularization term promotes solutions that have high a priori probability. In this dissertation, different reconstruction problems in computer graphics are shown to be instances of a common class of optimization problems which can be solved using a uniform algorithmic framework. Moreover, it is shown that similar optimization methods can also be used to solve data-based modeling problems, where the amount of information that can be obtained from measurements is insufficient for accurate reconstruction. As real-world examples of reconstruction problems, sparsity and group sparsity methods are presented for radio interferometric image reconstruction in static and time-dependent settings. As a modeling example, analogous approaches are investigated to automatically create volumetric models of astronomical nebulae from single images based on symmetry assumptions.

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