Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics: Dissertation (Paperback) (ISBN-13: 9783735742995)

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.

Author: Stephan Wenger
ISBN-10: 3735742998
ISBN-13: 9783735742995
Publisher: Bod - Books on Demand
Language: English
Published: 07/02/2014
Pages: 198
Format: Paperback
Weight: 0.53lbs
Size: 8.27h x 5.83w x 0.42d