Journal of Jilin University Science Edition

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Improved Snake Model in the Depression Boundary Detection

YAN Yang1,2, LI Yan bo3, WANG Yunji2, HUANG Wenbo1,2   

  1. 1. Institute of Computer Science and Technology, Changchun Normal University, Changchun 130032, China;2. College of Communication Engineering, Jilin University, Changchun 130012, China;3. College of Mathematics, Jilin University, Changchun 130012, China
  • Received:2013-03-08 Online:2013-09-26 Published:2013-09-17
  • Contact: LI Yan bo E-mail:liyanbo@jlu.edu.cn

Abstract:

We introduced a weight which can automatically control the external force of the Snake model is proportional to the size of the image gradient. Then we used Laplace operator to extend the gradient information to further area, thus expanding the search range of the Snake evolution curve so as to make the evolution curve get into the depressed area under the weighted external force. In OpenCV, the experiments show that improved Snake model can converge to the depression boundary of the target, by which convergence speed is increased. Thus our model solves the difficulty to capture depression border compared with the original Snake model.

Key words: image segmentation, edge detection, Snake model, Laplace operator

CLC Number: 

  • TP391.41