[Forschungsseminar-BSV] Forschungsseminar Computergrafik, Bildverarbeitung und Visualisierung

[Vanessa Kretzschmar]

E I N L A D U N G

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zum Forschungsseminar 'Computergrafik, Bildverarbeitung und Visualisierung'

     am Mittwoch, den 18. Mai 2022, 13:15 Uhr,
     im Raum P-701 im Paulinum am Augustusplatz, sowie über eine 
Webkonferenz.
     (https://conf.fmi.uni-leipzig.de/b/van-bwb-jil-vk2)

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Wir hören einen Vortrag von

     Dominik Vietinghoff

mit dem Titel:

     'Detecting Critical Points in 2D Scalar Field Ensembles Using 
Bayesian Inference'

zum Inhalt:

     In an era of quickly growing data set sizes, information reduction 
methods
     such as extracting or highlighting characteristic features become more
     and more important for data analysis. For single scalar fields, 
topological
     methods can fill this role by extracting and relating critical points.
     While such methods are regularly employed to study single scalar 
fields,
     it is less well studied how they can be extended to uncertain data, as
     produced, e.g., by ensemble simulations. Motivated by our previous work
     on visualization in climate research, we study new methods to 
characterize
     critical points in ensembles of 2D scalar fields. Previous work on this
     topic either assumed or required specific distributions, did not 
account
     for uncertainty introduced by approximating the underlying latent
     distributions by a finite number of fields, or did not allow to answer
     all our domain experts' questions. In this work, we use Bayesian 
inference
     to estimate the probability of critical points, either of the original
     ensemble or its bootstrapped mean. This does not make any assumptions
     on the underlying distribution and allows to estimate the sensitivity
     of the results to finite-sample approximations of the underlying
     distribution. We use color mapping to depict these probabilities 
and the
     stability of their estimation. The resulting images can, e.g., be used
     to estimate how precise the critical points of the mean-field are. We
     apply our method to synthetic data to validate its theoretical 
properties
     and compare it with other methods in this regard. We also apply our
     method to the data from our previous work, where it provides a more
     accurate answer to the domain experts' research questions.

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

Mit freundlichen Grüßen
Vanessa Kretzschmar
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