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Title:Bayesian multisensory perception
Authors: Timothy Hospedales ; Sethu Vijayakumar
Date:Jun 2006
Publication Title:NIPS2006
We investigate a solution to the problem of multi-sensor perception by formulating it in the framework of Bayesian model selection. Humans robustly integrate and segregate 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, principled Bayesian solution which accounts for both integration and segregation by reasoning explicitly about data association in a probabilistic framework. Unsupervised learning of such a model with EM is illustrated for a real world audio-visual application.
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author = { Timothy Hospedales and Sethu Vijayakumar },
title = {Bayesian multisensory perception},
year = 2006,
month = {Jun},
url = {},

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