A faster converging snake algorithm to locate object boundaries

Mustafa Sakalli, Kin Man Lam, Hong Yan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1182-1191
Number of pages10
JournalIEEE Transactions on Image Processing
Issue number5
Publication statusPublished - 1 May 2006


  • Active contour model
  • Boundary detection
  • Fast greedy snake algorithm (FGSA)
  • Greedy snake algorithm (GSA)
  • Locating human face boundaries

ASJC Scopus subject areas

  • Software
  • General Medicine
  • Computer Graphics and Computer-Aided Design


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