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Contour Segmentation Based on GVF Snake Model and Contourlet Transform

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Intelligent Computing Theories and Technology (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7996))

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Abstract

A contour segmentation algorithm is proposed based on GVF Snake model and Contourlet transform. Firstly, object contours of images can be obtained based on Contourlet Transform, and those contours will be identified as the initial contour of GVF Snake model. Secondly, GVF Snake model is used to detect the contour edge of human gait motion. Experimental results show that the proposed method can extract the edge feature accurately and efficiently.

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Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng, 475001, China

    Xinhong Zhang & Fan Zhang

  2. Software School, Henan University, Kaifeng, 475001, China

    Xinhong Zhang

  3. College of Computer and Information Engineering, Henan University, Kaifeng, 475001, China

    Kun Cai, Fan Zhang & Rui Li

Authors
  1. Xinhong Zhang
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  2. Kun Cai
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  3. Fan Zhang
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  4. Rui Li
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680-749 #7-413, San 29, Muger Dong, Ulsan, South Korea

    Kang-Hyun Jo

  3. Guangxi University for Nationalities, 530006, Nanning, Guangxi, China

    Yong-Quan Zhou

  4. School of Computer Science and Engineering, Inha University, 402-751, Incheon, South Korea

    Kyungsook Han

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhang, X., Cai, K., Zhang, F., Li, R. (2013). Contour Segmentation Based on GVF Snake Model and Contourlet Transform. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_58

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  • DOI: https://doi.org/10.1007/978-3-642-39482-9_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39481-2

  • Online ISBN: 978-3-642-39482-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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