Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics: Dissertation - Softcover
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.
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About the Author
Stephan Wenger received his Diplom degrees in physics and computer science from TU Braunschweig, Germany. In his work as a computer graphics researcher, he tries to bridge the gap between these disciplines to create novel and appealing visualizations for physical phenomena.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 196 pp. Englisch. Seller Inventory # 9783735742995
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783735742995
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Taschenbuch. Condition: Neu. Regularized Optimization Methods for Reconstruction and Modeling in Computer Graphics | Dissertation | Stephan Wenger | Taschenbuch | 196 S. | Englisch | 2014 | Books on Demand GmbH | EAN 9783735742995 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 105221360
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