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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Suzanne M. Mahoney and Kathryn Blockmond Laskey and Ed Wright and Keung Chi Ng (2000) Measuring performance for situation assessment. MSS National Symposium on Sensor Data Fusion
Kathryn Blackmond Laskey (2003) MEBN: A Logic for Open-World Probabilistic Reasoning. Department of Systems Engineering and Operations Research, Georg Mason University. MS4A6.
Kathryn Laskey and Ghazi Alghamdi and Xun Wang and Daniel Barbara and Tom Shackelford and Ed Wright and Julie Fitzgerald (2004) Detecting Threatening Behavior Using Bayesian Networks. Conference on Behavioral Representation in Modeling and Simulation.
Bruce D’Ambrosio and Masami Takikawa and Daniel Upper (2003) Representation for Dynamic Situation Modeling. Information Extraction and Transport, Inc.
Vitor Santo Costa and David Page and Maleeh Qazi and James Cussens (2003) CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. International Conference on Uncertainty in Artificial Intelligence.
Uri N. Lerner (2002) Hybrid bayesian network for resoning about complex systems. Department of Computer Science, Stanford University.
Daphne Koller and Avi Pfeffer (1997) Object-Oriented Bayesian Networks. Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), 302–313.
Daphne Koller and Avi Pfeffer (1998) Probabilistic frame-based systems. Fifteenth National Conference on Artificial Intelligence (AAAI-98), 580–587.
Avi Pfeffer and Daphne Koller and Brian Milch and Ken T. Takusagawa (1999) SPOOK: A system for probabilistic object-oriented knowledge representation. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), 541–550.
Suzanne M. Mahoney and Kathryn Blackmond Laskey (1997) Network Fragments: Representing Knowledge for Construction Probabilistic Models. Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97).
Suzanne M. Mahoney and Kathryn Blackmond Laskey (1998) Constructiong Situation Specific Belief Networks. Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), 370–378.
Suzanne M. Mahoney and Kathryn Blackmond Laskey (2003) Knowledge and Data Fusion in Probabilistic Networks. Journal of Machine Learning Research Vol. 4.
Bernd Neumann (2002) Conceptual Framework for High Level Vision. Department of Informatics, University of Hamburg, FBI-HH-B-241/02.
Bernd Neumann and Ralf Moeller (2004) On Scene Interpretation in DL. Department of Informatics, University of Hamburg, FBI-B-257/04.
H. Langseth and T. Nielsen (2003) Fusion of domain knowledge with data for structural learning in object oriented domains. Journal of Machine Learning Research, Vol. 4, 339–368.
Olav Bangso and Pierre-Henri Wuillemin (2000) Object Oriented Bayesian Networks: A Framework for Topdown Specification of Large Bayesian Networks and Repetitive Structures. Aalborg University, CIT-87.2-00-obphw1.
Raymond D. Rimey and Christopher M. Brown (1994) Control of Selective Perception Using Bayes Nets and Decision Theory. International Journal of Computer Vision, Vol 17, 173–209.
J. Pearl (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-35262-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35258-7
Online ISBN: 978-3-540-35262-4
eBook Packages: Business and EconomicsEconomics and Finance (R0)