An algorithm based on augmented Lagrangian method for generalized gradient vector flow computation

Dongwei Ren, Wangmeng Zuo, Xiaofei Zhao, Hongzhi Zhang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


We propose a novel algorithm for the fast computation of generalized gradient vector flow (GGVF) whose high cost of computation has restricted its potential applications on images with large size. We reformulate the GGVF problem as a convex optimization model with equality constraint. Our approach is based on a variable splitting method to obtain an equivalent constrained optimization formulation, which is then addressed with the inexact augmented Lagrangian method (IALM). To further enhance the computational efficiency, IALM is incorporated in a multiresolution approach. Experiments on a set of images with a variety of sizes show that the proposed method can improve the computational speed of the original GGVF by one or two order of magnitude, and is comparable with the multigrid GGVF (MGGVF) method in terms of the computational efficiency.
Original languageEnglish
Title of host publicationPattern Recognition - Chinese Conference, CCPR 2012, Proceedings
Number of pages8
Publication statusPublished - 10 Oct 2012
Event2012 5th Chinese Conference on Pattern Recognition, CCPR 2012 - Beijing, China
Duration: 24 Sep 201226 Sep 2012

Publication series

NameCommunications in Computer and Information Science
Volume321 CCIS
ISSN (Print)1865-0929


Conference2012 5th Chinese Conference on Pattern Recognition, CCPR 2012


  • augmented Lagrangian method
  • convex optimization
  • Generalized gradient vector flow
  • multiresolution method

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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