期刊文献+

Topography Image Segmentation Based on Improved Chan-Vese Model 被引量:5

Topography Image Segmentation Based on Improved Chan-Vese Model
下载PDF
导出
摘要 Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per)brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established. Aiming to solve the inefficient segmentation in traditional C-V model for complex topography image and time-consuming process caused by the level set function solving with partial differential, an improved Chan-Vese model is presented in this paper. With the good per)brmances of maintaining topological properties of the traditional level set method and avoiding the numerical so- lution of partial differential, the same segmentation results could be easily obtained. Thus, a stable foundation tbr rapid segmenta- tion-based on image reconstruction identification is established.
出处 《Computer Aided Drafting,Design and Manufacturing》 2013年第2期13-16,共4页 计算机辅助绘图设计与制造(英文版)
关键词 improved Chan-Vese model topography reconstruction image segmentation improved Chan-Vese model topography reconstruction image segmentation
  • 相关文献

参考文献5

  • 1Min sea. Based on the Regional Level Set Method for Image Segmentation Studies [D]. China Science and Technology University, 2010.
  • 2Sethian J A. Level Set Methods and Fast Marching Methods [M]. Cambridge United Kingdom, Cambridge University Press, 1999.
  • 3Li Jun. Image Segmentation Method Based on Curve Evolution and Application [D]. Shanghai, Shanghai Jiaotong University, 2001.
  • 4Aubert G. and Kornprobst P. Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations [M]. Springer, New York, 2002.
  • 5Zhao Minrong. Research to Camouflage Equipment Exterior Design Method Based on the Topography Image [D]. Air Force Engineering University, 2010.

同被引文献49

  • 1王孙安,郭子龙.混沌免疫模糊聚类算法在图像边缘检测中的应用[J].西安交通大学学报,2004,38(7):712-716. 被引量:9
  • 2刘存良,潘振宽,郑永果,端金鸣,张峰.两种保持符号距离函数的水平集分割方法[J].吉林大学学报(工学版),2013,43(S1):115-119. 被引量:2
  • 3Moreno J C, Surya P V B, Proenqa H, et al. Fast and globally convex multiphase active contours for brain MRI segmentation [J]. Computer Vision and Image Understanding, 2014, 125(2): 237-250.
  • 4Osher S, Sethian J A. Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi formulation [J]. Journal of Computational Physics, 1988, 79(1): 12-49.
  • 5Vese L A, Chan T F. A multiphase level set framework tbr image segmentation using the Mumford and Shah model [J]. International Journal of Computer Vision, 2002, 50(3): 271-293.
  • 6Bogovic J A, Prince J L, Bazin P L. A multiple object geometric deformable model for image segmentation [J]. Computer Vision and linage Understanding, 2013, 117(2) 145-157.
  • 7Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts [J]. IEEE Transactios on Pattern Analysis and Machine Intelligence, 2001, 23: 1222-1239.
  • 8Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts? [J]. IEEE Transactios on Pattern Analysis and Machine Intelligence, 2004, 26(2): 147-159.
  • 9Boykov Y, Funka-Lea G. Graph cuts and efficient N-D image segmentation [J]. International Journal of Computer Vision, 2006, 70(2): 109-131.
  • 10Zeng Yun, Samaras D, Chen Wei, et al. Topology cuts: a novel min-cut/maxflow algorithm for topology preserving segmentation in ND images [J]. Computer Vision and Image Understanding, 2008, 112(1 ): 81-90.

引证文献5

二级引证文献33

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部