Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25048
Title: An algorithm based on augmented Lagrangian method for generalized gradient vector flow computation
Authors: Ren, D
Zuo, W
Zhao, X
Zhang, H
Zhang, D 
Keywords: Augmented Lagrangian method
Convex optimization
Generalized gradient vector flow
Multiresolution method
Issue Date: 2012
Source: Communications in computer and information science, 2012, v. 321 CCIS, p. 170-177 How to cite?
Journal: Communications in Computer and Information Science 
Abstract: 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.
Description: 2012 5th Chinese Conference on Pattern Recognition, CCPR 2012, Beijing, 24-26 September 2012
URI: http://hdl.handle.net/10397/25048
ISBN: 9783642335051
ISSN: 1865-0929
DOI: 10.1007/978-3-642-33506-8_22
Appears in Collections:Conference Paper

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