[Forschungsseminar-BSV] Forschungsseminar Computergrafik, Bildverarbeitung und Visualisierung

[Patrick Oesterling]

E I N L A D U N G

zum Forschungsseminar Computergrafik, Bildverarbeitung und 
Visualisierung am Mittwoch den 09. Mai 2012, 13:15 Uhr, Raum 1-22 in der 
Johannisgasse 26.


Wir hören jeweils einen Vortrag von:

Silvia Born
Innovation Center Computer Assisted Surgery (ICCAS), Universität Leipzig

mit dem Titel

"Visual 4D MRI Blood Flow Analysis with Line Predicates "


zum Inhalt:

4D MRI is an in vivo flow imaging modality which has the potential to 
significantly enhance diagnostics and therapy of cardiovascular 
diseases. However, current analysis methods demand too much time and 
expert knowledge in order to apply 4D MRI in the clinics or larger 
clinical studies. One missing piece are methods allowing to gain a quick 
overview of the flow data's main properties. In this talk, I present a 
line predicate approach that sorts precalculated integral lines (which 
capture the complete flow dynamics) into bundles with similar 
properties. Several streamline and pathline predicates allow to 
structure the flow according to various useful features, such as, e.g., 
velocity distribution, vortices, and flow paths. The user can combine 
these predicates flexibly and by that create flow structures that help 
to gain overview and carve out special features of the current dataset. 
Results are shown by means of several 4D MRI datasets of healthy and 
pathological aortas.


und

Steven Schlegel
Universität Leipzig

mit dem Titel

"On the Interpolation of Data with Normally Distributed Uncertainty for 
Visualization"


zum Inhalt:

In many fields of science or engineering, we are confronted with 
uncertain data. For that reason, the visualization of uncertainty 
received a lot of attention, especially in recent years. In the majority 
of cases, Gaussian distributions are used to describe uncertain 
behavior, because they are able to model many phenomena encountered in 
science. Therefore, in most applications uncertain data is (or is 
assumed to be) Gaussian distributed. If such uncertain data is given on 
fixed positions, the question of interpolation arises for many 
visualization approaches. Therefore, we analyze the effects of the usual 
linear interpolation schemes for visualization of Gaussian distributed 
data. In addition, we demonstrate that methods known in geostatistics 
and machine learning have favorable properties for visualization 
purposes in this case.



Alle Interessierten sind im Namen von Prof. Dr. Scheuermann herzlich 
eingeladen.

Mit freundlichen Grüßen,
Patrick Oesterling