Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17279
Title: A faster converging snake algorithm to locate object boundaries
Authors: Sakalli, M
Lam, KM 
Yan, H
Keywords: Active contour model
Boundary detection
Fast greedy snake algorithm (FGSA)
Greedy snake algorithm (GSA)
Locating human face boundaries
Issue Date: 2006
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2006, v. 15, no. 5, p. 1182-1191 How to cite?
Journal: IEEE transactions on image processing 
Abstract: A different contour search algorithm is presented in this paper that provides a faster convergence to the object contours than both the greedy snake algorithm (GSA) and the fast greedy snake (FGSA) algorithm. This new algorithm performs the search in an alternate skipping way between the even and odd nodes (snaxels) of a snake with different step sizes such that the snake moves to a likely local minimum in a twisting way. The alternative step sizes are adjusted so that the snake is less likely to be trapped at a pseudo-local minimum. The iteration process is based on a coarse-to-fine approach to improve the convergence. The proposed algorithm is compared with the FGSA algorithm that employs two alternating search patterns without altering the search step size. The algorithm is also applied in conjunction with the subband decomposition to extract face profiles in a hierarchical way.
URI: http://hdl.handle.net/10397/17279
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2006.871401
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