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2010

Local Gaussian Processes for Pose Recognition from Noisy Inputs

10 years 11 months ago
Local Gaussian Processes for Pose Recognition from Noisy Inputs
Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they are limited due to their inability to model multi-modality caused by ambiguities and varying noise in the data set. For this reason techniques employing mixtures of local Gaussian processes have been proposed to allow multi-modal functions to be predicted accurately [11]. These techniques rely on the calculation of nearest neighbours in the input space to make accurate predictions. However, this becomes a limiting factor when image features are noisy due to changing backgrounds. In this paper we propose a novel method that overcomes this limitation by learning a logistic regression model over the input space to select between the local Gaussian processes. Our proposed method is more robust to a noisy input space than a nearest neighbour approach and provides a better prior over each Gaussian process predictio...
Martin Fergie, Aphrodite Galata
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where BMVC
Authors Martin Fergie, Aphrodite Galata
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