Title :
Stochastic processes in vision: from Langevin to Beltrami
Author_Institution :
Dept. of Appl. Math., Tel Aviv Univ., Israel
Abstract :
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
Keywords :
Brownian motion; computer vision; probability; stochastic processes; Brownian motion; Fokker-Planck equation; Riemannian surface; diffusion processes; low level vision; probability distribution; stochastic processes; Diffusion processes; Image analysis; Information geometry; Kernel; Laplace equations; Linearity; Mathematics; Nonlinear equations; Probability distribution; Stochastic processes;
Conference_Titel :
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1143-0
DOI :
10.1109/ICCV.2001.937531
