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dc.contributor.authorHou, G.
dc.contributor.authorPan, H.
dc.contributor.authorZhao, R.
dc.contributor.authorHao, Z.
dc.contributor.authorLiu, Wan-Quan
dc.identifier.citationHou, 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.

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.

dc.titleImage segmentation via the continuous max-flow method based on chan-vese model
dc.typeConference Paper
dcterms.source.titleCommunications in Computer and Information Science
dcterms.source.seriesCommunications in Computer and Information Science
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available

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