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  • 标题:On segmentation model for vector valued images and fast iterative solvers
  • 本地全文:下载
  • 作者:Noor Badshah ; Fahim Ullah ; Matiullah
  • 期刊名称:Advances in Difference Equations
  • 印刷版ISSN:1687-1839
  • 电子版ISSN:1687-1847
  • 出版年度:2018
  • 卷号:2018
  • 期号:1
  • 页码:221
  • DOI:10.1186/s13662-018-1669-9
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:In this paper, we propose a new convex variational model for segmentation of vector valued images. The data term of the proposed model is based on the coefficient of variation, which works well in vector valued images having intensity inhomogeneity. Due to convexity of the model, it is independent of the placement of initial contour. Better performance of the proposed model can be seen from experimental results qualitatively and quantitatively. Images in practice are of large sizes, which makes numerical methods more important. In this paper, we also develop fast and stable numerical methods for solution of partial differential equation arisen from the minimization of the proposed model. We have developed a novel multigrid method based on a locally supported smoother. The proposed method is compared with the existing methods in terms of iterations and CPU time for vector valued images having large sizes.
  • 关键词:Active contours ; Vector valued images ; Level sets ; Partial differential equations ; AOS method ; MG method ; Jacobi method ; Gauss Seidal method
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