Speaker: Hans-Christian Hege
Title: Uncertain Isocontours
Almost all scientific data is affected by uncertainty. Visualization techniques that consider uncertainties therefore are urgently needed. In this talk I will focus on scalar fields as input data. For analysis of such fields, usually topological or geometrical features are extracted and displayed. The most prominent features in scalar fields are isocontours. A means to describe how errors in the input data are amplified during feature extraction is numerical condition. Applying this to isocontours, the sensitivity of isocontours to changes in the input data can be computed and displayed. Furthermore, the average condition number can aid the selection of isovalues that correspond to isocontours that are particularly robust.
Discretely sampled uncertain scalar fields can be modeled using discrete random fields. This allows us to define 'uncertain isocontours'. Two different probabilistic measures are introduced to characterize these fuzzy objects. Furthermore, interactive visualization methods are adapted to display them. The methods are illustrated using 2D and 3D data sets from medical and climate research.
About the speaker:
The head of Visualization and Data Analysis Department at the Zuse Institute in Berlin. He is a co-founder of mental images as well as Amira. His work spans interactive medical visualization and analysis, vector and flow visualization, as well as mathematical visualization.