Algorithms for stochastic approximations of curvature flows

Gozde Unal, Delphine Nain, Gerard Ben-Arous, Nahum Shimkin, Allen Tannenbaum, Ofer Zeitouni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Curvature flows have been extensively considered from a deterministic point of view. They have been shown to be useful for a number of applications including crystal growth, flame propagation, and computer vision. In some previous work [1], we have described a random particle system, evolving on the discretized unit circle, whose profile converges toward the Gauss-Minkowsky transformation of solutions of curve shortening flows initiated by convex curves. The present note shows that this theory may be implemented as a new way of evolving curves and as a possible alternative to level set methods.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Pages651-654
Number of pages4
Volume2
StatePublished - 2003
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: Sep 14 2003Sep 17 2003

Other

OtherProceedings: 2003 International Conference on Image Processing, ICIP-2003
CountrySpain
CityBarcelona
Period9/14/039/17/03

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Unal, G., Nain, D., Ben-Arous, G., Shimkin, N., Tannenbaum, A., & Zeitouni, O. (2003). Algorithms for stochastic approximations of curvature flows. In IEEE International Conference on Image Processing (Vol. 2, pp. 651-654)