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文章基本信息

  • 标题:Local and Global Region-Based Curve Evolutions for Image Segmentation
  • 本地全文:下载
  • 作者:Haiyan Zhang ; Shangbing Gao ; Yufu Zhu
  • 期刊名称:The Open Cybernetics & Systemics Journal
  • 电子版ISSN:1874-110X
  • 出版年度:2014
  • 卷号:8
  • 期号:1
  • 页码:951-956
  • DOI:10.2174/1874110X01408010951
  • 出版社:Bentham Science Publishers Ltd
  • 摘要:

    In this paper, we propose a novel region-based active contour model in a variational level set formulation. The energy functional for the proposed model consists of two terms, i.e., global term, local term. Therefore, the combination of these two forces allows for flexible initialization of the contours. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase formulation. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with recent popular local image fitting (LIF) model also show that our model is less sensitive to the location of initial contour.

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