Diffusion processes which are widely used in low level vision are presented as a result of an underlying stochastic process. The short-time non-linear diffusion is interpreted as a Fokker-Planck equation which governs the evolution in time of a probability distribution for a Brownian motion on a Riemannian surface. The non linearity of the diffusion has a direct relation to the geometry of the surface. A short time kernel to the diffusion as well as generalizations are found.
Stochastic processes in vision: from Langevin to Beltrami
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 1 ; 288-293 vol.1
2001-01-01
492876 byte
Conference paper
Electronic Resource
English
Stochastic Processes in Vision: From Langevin to Beltrami
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