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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2004: PRICAI 2004: Trends in Artificial Intelligence pp 778–786Cite as

Gradient Vector Flow Snake with Embedded Edge Confidence

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3157))

Abstract

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly in locating object boundaries. Problems associated with initialization and poor convergence to boundary concavities have limited their utility. Gradient vector flow (GVF) snake solved both problems successfully. However, boundaries in noisy images are often blurred even destroyed with smoothing and false results usually occur when such images are processed even with GVF snake model. We have incorporated embedded edge confidence (EEC) into GVF snake model. The improved method can solve this problem when noisy images were processed.

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Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, 200030, P.R. China

    Yuzhong Wang & Jie Yang

Authors
  1. Yuzhong Wang
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  2. Jie Yang
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Department of Computer Science, Auckland University of Technology, 1020, Auckland, New Zealand

    Hans W. Guesgen

  3. Artificial Intelligence Technology Centre, Auckland University of Technology, Auckland, New Zealand

    Wai-Kiang Yeap

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© 2004 Springer-Verlag Berlin Heidelberg

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Wang, Y., Yang, J. (2004). Gradient Vector Flow Snake with Embedded Edge Confidence. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_82

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  • DOI: https://doi.org/10.1007/978-3-540-28633-2_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22817-2

  • Online ISBN: 978-3-540-28633-2

  • eBook Packages: Springer Book Archive

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