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Könyv Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics Stephan Wenger

Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics

Dissertation

Szerző: Stephan Wenger
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Books on Demand
Elérhetőség: Beszállítói készleten
Küldés 8-11 napon belül
4 846 Ft
The field of computer graphics deals with virtual representations of the real world. These can be ob...

Információk a könyvről

Szerző
Stephan Wenger
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2014
oldal
198
EAN
9783735742995
ISBN
3735742998
Enbook ID
02784605
Kiadó
Books on Demand
Súly
240
Méretek
148 x 210 x 11

Teljes leírás

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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