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

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

          
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About the Book

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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Product Details
  • ISBN-13: 9783735742995
  • Publisher: Books on Demand
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Dissertation
  • Width: 148 mm
  • ISBN-10: 3735742998
  • Publisher Date: 02 Jul 2014
  • Height: 210 mm
  • No of Pages: 198
  • Spine Width: 11 mm
  • Weight: 245 gr


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