[1]张荣国,刘小君,党伟超,等.多目标轮廓MumfordShah水平集提取[J].智能系统学报,2011,6(04):360-366.
 ZHANG Rongguo,LIU Xiaojun,DANG Weichao,et al.MumfordShah level set method for multiobjective contour extraction[J].CAAI Transactions on Intelligent Systems,2011,6(04):360-366.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第6卷
期数:
2011年04期
页码:
360-366
栏目:
出版日期:
2011-08-25

文章信息/Info

Title:
MumfordShah level set method for multiobjective contour extraction
文章编号:
1673-4785(2011)04-0360-07
作者:
张荣国12刘小君2党伟超1刘焜2
1.太原科技大学 计算机科学与技术学院,山西 太原 030024;
 2.合肥工业大学 机械与汽车工程学院,安徽 合肥 230009
Author(s):
ZHANG Rongguo12 LIU Xiaojun2 DANG Weichao1 LIU Kun2
1. School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China; 2. School of Mechanical and Automotive Engineering, Hefei University of Technology, Hefei 230009, China
关键词:
MumfordShah模型水平集方法多目标轮廓能量方程
Keywords:
MumfordShah model level set method multiobjective contours energy equation
分类号:
TP391
文献标志码:
A
摘要:
目标轮廓的快速检测进而提取其几何形状,在图形图像处理中有着重要的作用.提出了一种多目标轮廓的水平集提取方法,对基于MumfordShah模型的CV方法从两方面进行了改进:增加梯度矢量场和曲线法方向的融合作为边界吸引场,生成可以驱动主动轮廓向边缘进化的双向几何变形流,保留原图像分布信息作为区域进化能,解决未考虑局部几何信息造成的区域能量捕捉信息不全,或边缘梯度场和演化曲线法线方向正交时无法实现拓扑结构变化的缺陷;对水平集函数进行修正,使得它在收敛过程中能自动进行调整,确保其满足符号距离函数的要求,扩大初始化前迭代搜索区域,减少初始化次数,提高收敛效率;最后给出所提方法的数字化求解方案.实验表明该方法可行且具有较好的鲁棒性.
Abstract:
Fast detection of objective contours and extraction of its geometric shape have important roles in graphics and image processing. Based on the MumfordShah model, a novel level set method for multiobjective contour extraction was presented. First, the gradient vector field was combined with normal direction of the curves as boundary abstracted fields, so as to generate a bidirectional geometric deformable flow field which can drive active contours evolving towards the boundary from inside or outside edges. Furthermore, the distributed information of the image would be left as area evolution energy. This method can solve problems that arise when area energy information is lost because local geometric information isn’t considered, or when topological structure should not be changed because the gradient vector field is orthogonal with normal direction. Then the level set function was modified so that it could change adaptively in curve convergence. Other reasons for this modification were to make sure that the level set changes could maintain signal distance function, the search area could be covered sufficiently before reinitialization, and the iterative number could be decreased. The convergence efficiency was also raised. Finally, a numerical solving scheme was given. Experimental results illustrate that the method proposed in this paper is feasible and robust. 

参考文献/References:

[1]KASS M, WITKIN A, TERZOPOULS D. Snake: active contour models[J]. International Journal of Computer Vision, 1987, 1(4): 321331.
[2]XU C, PRINCE J L. Snake, shapes, and gradient vector flow[J]. IEEE Trans on Image Processing, 1998, 7: 359369.
[3]NING Jifang, WU Chengke, LIU Shigang, et al. NGVF: an improved external force field for active contour model[J]. Pattern Recognition Letters, 2007, 28(1): 5863.
[4]SAKALLI M, LAM K M, YAN H. A faster converging snake algorithm to locate object boundaries[J]. IEEE Transactions on Image Processing, 2006, 15(5): 11821191. 
[5]SUM K W, CHEUNG P Y S. Boundary vector for parametric active contours[J]. Pattern Recognition, 2007, 40(6): 16351645.
[6]XIE X H, MIRMEHDI M. MAC: magnetostatic active contour model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(4): 632646.
[7]OSHER 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.
[8]CASSELLES V, KIMMEL R, SAPIRO G. Geodesic active contours[J]. International Journal of Computer Vision, 1997, 22: 6179.
[9]OSHER S, PARAGIOS N. Geometric level set methods in imaging, vision, and graphics[M]. Berlin: Springer Verlag, 2003: 4357.
[10]PARAGIOS N, DERICH R. Geodesic active regions for supervised texture segmentation[C]//IEEE International Conference on Computer Vision. Kerkyra, Greece, 1999, 2: 926932.
[11]PARAGIOS N, MELLINA G O, RAMESH V. Gradient vector flow fast geometric active contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(3): 402407.
[12]CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2): 266277.
 [13]龚永义,罗笑南,黄辉,等.基于单水平集的多目标轮廓提取[J].计算机学报, 2007, 30(1): 120128.
GONG Yongyi, LUO Xiaonan, HUANG Hui, et al. Multiobjects extracted based on single level set[J]. Chinese Journal of Computers, 2007, 30(1): 120128.
[14]GAO S, TIEN D. Image segmentation and selective smoothing by using MumfordShah mode[J]. IEEE Transactions on Image Processing, 2005, 14(10): 15371549.
[15]李俊, 杨新, 施鹏飞. 基于MumfordShah模型的快速水平集图像分割方法[J].计算机学报, 2002, 25 (11): 11751183.
 LI Jun, YANG Xin, SHI Pengfei. A fast level set approach to image segmentation based on MumfordShah model[J]. Chinese Journal of Computers, 2002, 25 (11): 11751183.
[16]LI Chunming, XU Chenyang, GUI Changfeng, et al. Level set evolution without reinitialization: a new variational formulation[C]//IEEE International Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005, 1: 430436.
[17]杨莉,杨新. 基于区域划分的曲线演化多目标分割[J].计算机学报, 2004, 27(3): 420425.
 YANG Li, YANG Xin. Multiobject segmentation based on curve evolving and region division[J]. Chinese Journal of Computers, 2004, 27 (3): 420425.
[18]LIE J, LYSAKER M, TAI X C. A binary level set model and some applications for MumfordShah image segmentation[J]. IEEE Transactions on Image Processing, 2006, 15(5): 11711181.
 [19]TAI Xuecheng, LI Hongwei. A piecewise constant level set methods for elliptic inverse problems[J]. Applied Numerical Mthematics, 2007, 57(5/6/7): 686696.
 [20]叶伟,王远军. 基于MumfordShah 理论的最小生成树图像分割方法[J]. 计算机辅助设计与图形学学报,2009, 21(8): 11271133.
 YE Wei, WANG Yuanjun. MST image segmentation based on Mumfordshah theory method based on boundary and region information[J]. Journal of Computer—Aided Design & Computer Graphics, 2009, 21(8): 11271133.
[21]何宁,张朋.基于边缘和区域信息相结合的变分水平集图像分割方法[J].电子学报, 2009, 37 (10): 22152219.
HE Ning, ZHANG Peng. Variational level set image segmentation method based on boundary and region information[J]. Acta Electronic Sinica, 2009, 37(10): 22152219.
[22]张荣国, 刘小君, 王蓉, 刘焜. 自适应梯度矢量流轮廓提取方法研究[J].模式识别与人工智能, 2008, 21(6): 799805.
 ZHANG Rongguo, LIU Xiaojun, WANG Rong, LIU Kun. Adaptive gradient vector flow algorithm for boundary extraction[J]. Pattern Recognition and Artificial Intelligence, 2008, 21(6): 799805.

备注/Memo

备注/Memo:
收稿日期: 2010-07-15.
基金项目:国家自然科学基金资助项目(51075113). 
通信作者:张荣国. E-mail:rg_zh@163.com.
 作者简介:
张荣国,男,1964年生,教授, 博士,主要研究方向为图形图像处理、CAD/ CG和计算机支持的协同设计等.
刘小君,女,1965年生,教授, 博士,主要研究方向为数字化设计和图像处理.
党伟超,男,1974年生,副教授,主要研究方向为图像处理与信息系统.
更新日期/Last Update: 2011-09-30