Segmented snake for contour detection

Y. Y. Wong, Pong Chi YUEN*, Chong Sze TONG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

The active contour model, called snake, has been proved to be an effective method in contour detection. This method has been successfully employed in the areas of object recognition, computer vision, computer graphics and biomedical images. However, this model suffers from a great limitation, that is, it is difficult to locate concave parts of an object. In view of such a limitation, a segmented snake is designed and proposed in this paper. The basic idea of the proposed method is to convert the global optimization of a closed snake curve into local optimization on a number of open snake curves. The segmented snake algorithm consists of two steps. In the first step, the original snake model is adopted to locate the initial contour near the object boundary. In the second step, a recursive split-and-merge procedure is developed to determine the final object contour. The proposed method is able to locate all convex, concave and high curvature parts of an object accurately. A number of images are selected to evaluate the capability of the proposed algorithm and the results are encouraging.

Original languageEnglish
Pages (from-to)1669-1679
Number of pages11
JournalPattern Recognition
Volume31
Issue number11
DOIs
Publication statusPublished - Nov 1998

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Active contour model
  • Contour detection
  • Convex and concave parts
  • Global and local optimization
  • Open snake
  • Snake model

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