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» Modelling Smooth Paths Using Gaussian Processes
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ICASSP
2011
IEEE
13 years 2 months ago
A reversible jump MCMC algorithm for Bayesian curve fitting by using smooth transition regression models
This paper proposes a Bayesian algorithm to estimate the parameters of a smooth transition regression model. With in this model, time series are divided into segments and a linear...
Matthieu Sanquer, Florent Chatelain, Mabrouka El-G...
ECCV
2008
Springer
15 years 24 days ago
Edge-Preserving Smoothing and Mean-Shift Segmentation of Video Streams
Video streams are ubiquitous in applications such as surveillance, games, and live broadcast. Processing and analyzing these data is challenging because algorithms have to be effic...
Sylvain Paris
BMVC
1998
14 years 8 days ago
Multi-Scale 3-D Free-Form Surface Smoothing
A novel technique for multi-scale smoothing of a free-form 3-D surface is presented. Complete triangulated models of 3-D objects are constructed (through fusion of range images) a...
Farzin Mokhtarian, Nasser Khalili, Peter Yuen
DSMML
2004
Springer
14 years 4 months ago
Can Gaussian Process Regression Be Made Robust Against Model Mismatch?
Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), e...
Peter Sollich
UAI
2008
14 years 10 days ago
Modelling local and global phenomena with sparse Gaussian processes
Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3 ) scaling in computational time to O(nm2 ). Thus far, wo...
Jarno Vanhatalo, Aki Vehtari