Probabilistic Preference Measures
for Knowledge-based Scene Interpretation (PRAESINT)
Project Data
DFG-Projekt Ne 278/9-1
2007 - 2012
Project team at the University of Hamburg:
Bernd
Neumann (project co-leader)
Wilfried
Bohlken
Project team at the Hamburg University of Technology:
Ralf Möller
(project co-leader)
NN
Project Goals
The project deals with the development of generic computer methods for
the interpretation of everyday visual scenes, static or dynamic, such
as indoor scenes for elderly assistance or traffic scenes for
monitoring purposes. Scene interpretation of this kind requires both,
extensive knowledge about everyday occurrences which can be represented
by logic-based knowledge representation methods, and probabilistic
models for
uncertain interpretation decisions and predictions. In this project we
investigate a particular way for integrating probabilistic models with
formal knowledge representation for scene
interpretation. Logic-based inferences delineate the space of
consistent scene interpretations whereas probabilistic inferences
determine preferred interpretations among those which are logically
possible.