Algorithms for stochastic approximations of curvature flows

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Title: Algorithms for stochastic approximations of curvature flows
Author: Unal, Gozde ; Nain, Delphine ; Ben Arous, Gérard ; Shimkin, Nahum ; Tannenbaum, Allen R. ; Zeitouni, Ofer
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 G. Ben-Arous et al. (2002), 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.
Description: ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. DOI: 10.1109/ICIP.2003.1246764 Presented at ICIP-2003 : 2003 International Conference on Image Processing, September 14-17, 2003, Barcelona, Spain.
Type: Proceedings
ISSN: 1522-4880
ISBN: 0-7803-7750-8
Citation: Gozde Unal, Delphine Nain, Gerard Ben-Arous, Nahum Shimkin, Allen Tannenbaum, Ofer Zeitouni," Algorithms for stochastic approximations of curvature flows," International Conference on Image Processing, 2003, II - 651-4.
Date: 2003-09
Contributor: Georgia Institute of Technology. College of Computing
Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel
Georgia Institute of Technology. School of Electrical and Computer Engineering
University of Minnesota. School of Mathematics
New York University. Dept. of Mathematics
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Computer vision
Crystal growth
Stochastic processes

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