Feature-preserving flows: a stochastic differential equation´s view

Author

Unal, Gozde B. ; Krim, Hamid ; Yezzi, Anthony

Author_Institution

Dept. of Electr. & Comput. Eng., North Carolina State Univ., Raleigh, NC, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

896

Abstract

Evolution equations have proven to be useful in tracking fine to coarse features in a single level curve and/or in an image. We give a stochastic insight to a specific evolution equation, namely the geometric heat equation, and subsequently use this insight to develop a class of feature-driven diffusions. A progressive smoothing along desired features of a level curve is aimed at overcoming effects of noisy environment during feature extraction and denoising applications

Keywords

feature extraction; image restoration; noise; partial differential equations; smoothing methods; stochastic processes; tracking; evolution equations; feature extraction; feature-driven diffusion; feature-preserving flows; geometric heat equation; image denoising; image features tracking; image restoration; noisy environment; partial differential equation; progressive smoothing; single level curve; stochastic differential equation; Computer vision; Differential equations; Feature extraction; Filters; Level set; Noise reduction; Smoothing methods; Stochastic processes; Target tracking; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.901104

Filename

901104