PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian Processes of Nonlinear Diffusion Filtering
Ramunas Girdziusas and Jorma Laaksonen
In: International Joint Conference on Neural Networks, 31 Jul - 4 Aug 2005, Montreal, Canada.


Nonlinear diffusion filtering can be improved if viewed as Bayesian Gaussian process regression. We relate the covariance functions of the diffusion process outcome to the spatial diffusion operator and show how Bayesian evidence criterion can be utilized to determine the parameters of the nonlinear diffusivity and the optimal diffusion stopping time. Computational example is given where the nonlinear diffusion filtering outperforms typical Gaussian process regression.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1738
Deposited By:Jorma Laaksonen
Deposited On:28 November 2005