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What and Who
Title:Regularized optimization methods for reconstruction and modeling in computer graphics
Speaker:Stephan Wenger
coming from:TU Braunschweig, Germany
Speakers Bio:
Event Type:AG4 Talk
Visibility:D2, D4, MMCI
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Level:MPI Audience
Date, Time and Location
Date:Thursday, 20 November 2014
Duration:60 Minutes
Building:E1 4
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.

Name(s):Bertram Somieski
EMail:--email address not disclosed on the web
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  • Bertram Somieski, 11/18/2014 11:10 AM -- Created document.