Abstract-Band - Fakultät für Informatik, TU Wien - Technische ...

specialized as they need a lot of prior knowledge and often the results have
to be corrected manually. An alternative to automatic and semi-automatic
methods is to perform the segmentation manually. The main drawbacks of
this approach are that it is very tedious and time consuming, the user
knowledge has a very high impact on the results and it is very hard to
reproduce specific results. This work presents a tool that enables the user to
enhance results of automatic and semi-automatic algorithms and to do fast
manual segmentation of shapes of arbitrary topology from scratch. The tool
can deal with three and four dimensional image datasets captured by
different modalities. The segmentation is mesh based and performed with a
2D cut approach. To achieve a better alignment of the edges to a specific
shape the edge class based Sticky Edges algorithm is introduced.
Furthermore, well known mesh optimization algorithms were implemented to
accomplish better results. To achieve a faster segmentation of 4D datasets
two methods are presented. With the rst one the user can record its
interactions on one volume in the 4D dataset and apply them automatically to
the other volumes. The other one enables the user set an already segmented
mesh as start position for the segmentation of other volumes. The approach
presented in this work is up to 25 times faster than the evaluation approach.
Moreover, the mesh quality regarding smoothness, curvature and triangle
quality are at eye level with the evaluation meshes. The geometric distance to
the ground truth meshes is on average 2 mm and the normal deviation is
between 0.3 and 0.4 degree.
Axel Goldmann
Towards GPU Speech Coding
Studium: Masterstudium Medieninformatik
Betreuer: Associate Prof. Dr. Michael Wimmer
Speech transmission is the central service in many telecommunication
infrastructures. The encoding of many channels according to modern
standards requires a fair amount of processing capacity. With the recent GPU
product lines, powerful platforms have become available as supplement to
desktop CPUs. This thesis tries to leverage these developments and examines
the possibilities of general purpose GPU employment in the context of
speech coding. The speech codec used in the TETRA mobile radio system is
implemented using the CUDA programming model. The main question is,
how many channels can be encoded in real time on current GPUs. Results
show that through careful implementation and with some effort, a substial
number of channels can be processed. It seems however that modern
multicore CPUs are much better qualified for the task. The presented
optimizations are far from complete and further research directions are
suggested.
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