Abstract
We present an approach to probabilistic modelling of static and dynamic scenes for the purpose of scene interpretation and -prediction. Our system, utilizing Bayesian Network Fragments as relational extension to Bayesian networks, provides modelling in an object-oriented way, handling modular repetitivities and hierarchies within domains. We specify a knowledge-based framework, which maintains both partonomy- and taxonomy-hierarchies of entities, and describe an interpretation method exploiting these. The approach offers arbitrary reasoning facilities, where low level perceptive information as well as abstract context knowledge within scenes can be either given as evidence or queried.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lueders, P. (2006). Scene Interpretation Using Bayesian Network Fragments. In: Coping with Uncertainty. Lecture Notes in Economics and Mathematical Systems, vol 581. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35262-7_7
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DOI: https://doi.org/10.1007/3-540-35262-7_7
Publisher Name: Springer, Berlin, Heidelberg
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