Abstract
In this paper we propose a general framework to build a task oriented 3d object recognition system. To cope with noisy data under changing viewing conditions a 3d object recognition system has to acquire sensor data incrementally (active sensing) and has to choose appropriate actions to reduce the uncertainty in the recognition results (task driven recognition). To model the statistical behavior of the data we introduce Bayesian nets which model the relationship between objects and observable features. Furthermore, task oriented selection of the optimal action to reduce the uncertainty of recognition results is incorporated in the Bayesian net. This enables the integration of intelligent recognition strategies depending on the already acquired evidence into a robust and efficient 3d model based recognition system.
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© 1997 Springer-Verlag Berlin Heidelberg
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Krebs, B., Korn, B., Burkhardt, M. (1997). A 3d Object Recognition System with Decision Reasoning under Uncertainty. In: Paulus, E., Wahl, F.M. (eds) Mustererkennung 1997. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60893-3_18
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DOI: https://doi.org/10.1007/978-3-642-60893-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63426-3
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