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    Image segmentation via the continuous max-flow method based on chan-vese model

    Access Status
    Fulltext not available
    Authors
    Hou, G.
    Pan, H.
    Zhao, R.
    Hao, Z.
    Liu, Wan-Quan
    Date
    2018
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Hou, G. and Pan, H. and Zhao, R. and Hao, Z. and Liu, W. 2018. Image segmentation via the continuous max-flow method based on chan-vese model, in Wang Y. et al. (eds) Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757, pp. 232-242. Singapore: Springer.
    Source Title
    Communications in Computer and Information Science
    DOI
    10.1007/978-981-10-7389-2_23
    ISBN
    9789811073885
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/59762
    Collection
    • Curtin Research Publications
    Abstract

    The Chan-Vese model using variational level set method (VSLM) has been widely used in image segmentation, but its efficiency is a challenge problem due to high computation costs of curvature as well as the Eiknal equation constraint. In this paper, we propose a continuous Max-Flow (CMF) method based on discrete grap h cut approach to solve the VSLM for image segmentation. Firstly, we recast the original Chan-Vese model to a continuous max-flow problem via the primal-dual method and solve it using the alternating direction method of multipliers (ADMM). Then, we use the projection method to recover the continuous level set function for image segmentation expressed as a signed distance function. Finally, some numerical examples are presented to demonstrate the efficiency and accuracy of the proposed method.

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