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Book synopsis

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.

Book details

Book category Books in English Computing & information technology Computer programming / software development Programming & scripting languages: general

• Full title: Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics
• Subtitle: Dissertation
• Author: Stephan Wenger
• Language: English
• Binding: Paperback
• Number of pages: 198
• EAN: 9783735742995
• ISBN: 3735742998
• ID: 02784605
• Publisher: Books on Demand
• Weight: 240 g
• Dimensions: 210 × 148 × 11 mm
• Date of publishing: 02. July 2014

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