Abstract
In most object recognition systems, interactions between objects in a scene are ignored and the best interpretation is considered to be the set of hypothesized objects that matches the greatest number of image features. Visual and physical interactions, however, provide a rich source of information: occlusion explains why features might be unde-tected, and physical constraints ensure a realisable interpretation. We show how these interations can be easily modeled using a Bayesian network, and how the problem of interpretation can be cast as finding the most likely explanation for such a network.
The partial support of the Defense Research Projects Agency (ARPA Order No. C635) and the Office of Naval Research (grant N000149510521) is gratefully acknowledged.
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© 1997 Springer-Verlag Berlin Heidelberg
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Westling, M.F., Davis, L.S. (1997). Interpretation of complex scenes using Bayesian networks. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63931-4_216
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DOI: https://doi.org/10.1007/3-540-63931-4_216
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