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Abstract

This thesis describes the research results and contributions that have been
achieved during the author�s doctoral work. It is divided into two independent
parts, each of which is devoted to a particular research aspect.
The first part covers the true-to-detail creation of digital pieces of art,
so-called
relief sculptures, from given 3D models. The main goal is to limit the depth of
the
contained objects with respect to a certain perspective without compromising the
initial three-dimensional impression. Here, the preservation of significant
features
and especially their sharpness is crucial. Therefore, it is necessary to
overemphasize fine surface details to ensure their perceptibility in the more
complanate relief.Our developments are aimed at amending the flexibility and
user-friendliness during the generation process. The main focus is on providing
real-time solutions with intuitive usability that make it possible to create
precise, lifelike andaesthetic results. These goals are reached by a GPU
implementation, the use of efficient filtering techniques, and the replacement
of user defined parameters by adaptive values. Our methods are capable of
processing dynamic scenes and allow the generation of seamless artistic reliefs
which can be composed of multiple elements.
The second part addresses the analysis of repetitive structures, so-called
symmetries, within very large data sets. The automatic recognition of components
and their patterns is a complex correspondence problem which has numerous
applications ranging from information visualization over compression to
automatic
scene understanding. Recent algorithms reach their limits with a growing amount
of data, since their runtimes rise quadratically. Our aim is to make even
massive
data sets manageable. Therefore, it is necessary to abstract features and to
develop a suitable, low-dimensional descriptor which ensures an efficient,
robust, and purposive search. A simple inspection of the proximity within the
descriptor space helps to significantly reduce the number of necessary pairwise
comparisons. Our method scales quasi-linearly and allows a rapid analysis of
data sets which could not be handled by prior approaches because of their size.