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Structure Inference for Bayesian Multisensory Perception and Tracking

International Joint Conference on Artificial Intelligence (IJCAI 2007)

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hospedales-IJCAI2007.pdf (1.359Mb)
Date
01/2007
Author
Hospedales, Timothy
Cartwright, Joel
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Abstract
We investigate a solution to the problem of multisensor perception and tracking by formulating it in the framework of Bayesian model selection. Humans robustly associate multi-sensory data as appropriate, but previous theoretical work has focused largely on purely integrative cases, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, Bayesian solution to multi-sensor perception and tracking which accounts for both integration and segregation by explicit probabilistic reasoning about data association in a temporal context. Unsupervised learning of such a model with EM is illustrated for a real world audio-visual application.
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http://homepages.inf.ed.ac.uk/svijayak/publications/hospedales-IJCAI2007.pdf

http://hdl.handle.net/1842/3713
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