交互式图像分割算法的研究与应用
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摘要
图像分割是一种基础的图像处理技术,也是图像处理和计算机视觉领域中的难点问题。近年来,交互式分割方法受到了各领域学者的广泛关注。本文在整理、归纳和总结了几种交互式图像分割理论、方法的基础上,针对Live Wire算法中的最短路径求解、随机游走分割方法和滑降算法等问题进行深入研究,主要工作如下：
针对传统Live Wire算法运行速度较慢的缺点,提出了一种基于脉冲耦合神经网络(PCNN)的Live Wire算法,利用PCNN求解用户标记的边缘种子点之间的最短路径,提高了Live Wire的运算速度。尤其是当处理大分辨率图像时,速度优势明显。
在赤足足迹跟区压痕边缘检测的研究中,针对跟区压痕呈现弱边缘特性,提出了一种基于蚁群(ACO)寻找最短路径的Live Wire分割足迹跟区压痕提取的方法,并利用Sobel算子建立像素点间的代价函数,通过最小二乘算法进行椭圆拟合得到足迹参数,从而有效地提取了足迹跟区压痕边缘。
为提高传统随机游走算法的有效性和分割速度,提出了一种基于滑降算法的随机游走图像分割算法。首先利用滑降算法将图像进行初始分割,将每个小区域作为一个节点,然后采用万有引力思想代替传统节点间权值定义方式,最后,利用随机游走算法完成最终分割。该算法明显提高了图像分割的速度和精度。
针对智能交通系统中多车辆检测问题,提出了基于边缘检测的随机游走算法,对视频中车辆目标进行精确检测和分割。首先将背景差分与边缘信息相结合来检测运动车辆区域,然后根据检测车辆区域信息提取骨架结构,从中获取随机游走所需的有效标记点,最后采用随机游走算法实现车辆自动检测和精确分割。
针对滑降算法过分割现象,本文从两个方面对滑降算法进行了改进。一方面为多尺度形态学梯度滑降分割算法,利用多尺度形态学梯度,通过大小不同的结构元素提取图像梯度特征,获得梯度图像；然后利用滑降算法进行图像分割；同时,为了减少滑降算法的过分割现象,利用区域面积和区域相似性规则进行区域合并。另一方面将反馈脉冲耦合神经网络与滑降分割相结合,提出了一种新的MRI图像分割的特征提取算法。
Image Segmentation is a basic technique of image processing, and it is always one of the most difficult techniques in image processing and computer vision. In recent years, the researchers pay more and more attention to the interactive segmentation. On the base of arranging, summarizing and concluding interactive segmentation algorithm, some key theories and approaches in interactive segmentation and its applications are researched in this dissertation. In this dissertation, we attempt to have an in-depth investigation on the Live Wire algorithm, random walk and toboggan method. The main work is as follows:
Considering the speed disadvantage of the traditional Live Wire algorithm, a novel Live Wire algorithm based on Pusle Coupling Neural Network (PCNN) is proposed. PCNN is used to obtain the shortest path between the two points by user. The speed of operation is increased by proposed method, especially when dealing with a larger image, the rate reflected a clear advantage.
On the study of detection the footprint heel impression, this paper presents an improved Live Wire algorithm by considering the feature of weak edge for heel impression. Ant colony optimization (ACO) algorithm is used to find the shortest path. A new cost function is defined by Sobel operator. The least square method was used for ellipse fitting to obtain the parameters. The improved methods can effectively extract the information of the footprint heel impression.
In order to improve the efficiency and speed of random walk algorithm, a new random walk method based on toboggan is presented. Firstly, a graph is created which decomposes the image in scale and space using the concept of toboggan. In this way, we consider each of the regions as the nodes, the weights between graph-nodes is estimated by using the law of universal gravity. Then the label for object and background is drawn by user. Finally, this paper uses the theory of random walk algorithm to segment the image.
According to the multi-vehicle problem in Intelligence Transportation System, a vehicle detection algorithm is presented based on the combination of edge feature and random walk techniques. We used background subtraction and edge detection to obtain the moving area, then used morphological operations for vehicle skeleton extraction and get the seeds for random walk. Finally the accurate boundary of moving vehicles is detection by random walk.
Considering the over-segmentaion of the toboggan algorithm, two improve toboggan algorithms are proposed in this thesis. Firstly, a new toboggan is presented based on multi-scale morphological gradient. The gradient image is computed by using the multi-scale morphological gradient operation. It was obtained through the difference size of the structure elements from images gradient features. In order to reduce the over-segmentation of toboggan, the approach of region combination is used after toboggan segmentation. Secondly, combining the feedback pulse coupling neural network (FPCNN) with toboggan segmentation algorithm, this thesis presents a new MRI image segmentation feature extraction methods.
针对传统Live Wire算法运行速度较慢的缺点,提出了一种基于脉冲耦合神经网络(PCNN)的Live Wire算法,利用PCNN求解用户标记的边缘种子点之间的最短路径,提高了Live Wire的运算速度。尤其是当处理大分辨率图像时,速度优势明显。
在赤足足迹跟区压痕边缘检测的研究中,针对跟区压痕呈现弱边缘特性,提出了一种基于蚁群(ACO)寻找最短路径的Live Wire分割足迹跟区压痕提取的方法,并利用Sobel算子建立像素点间的代价函数,通过最小二乘算法进行椭圆拟合得到足迹参数,从而有效地提取了足迹跟区压痕边缘。
为提高传统随机游走算法的有效性和分割速度,提出了一种基于滑降算法的随机游走图像分割算法。首先利用滑降算法将图像进行初始分割,将每个小区域作为一个节点,然后采用万有引力思想代替传统节点间权值定义方式,最后,利用随机游走算法完成最终分割。该算法明显提高了图像分割的速度和精度。
针对智能交通系统中多车辆检测问题,提出了基于边缘检测的随机游走算法,对视频中车辆目标进行精确检测和分割。首先将背景差分与边缘信息相结合来检测运动车辆区域,然后根据检测车辆区域信息提取骨架结构,从中获取随机游走所需的有效标记点,最后采用随机游走算法实现车辆自动检测和精确分割。
针对滑降算法过分割现象,本文从两个方面对滑降算法进行了改进。一方面为多尺度形态学梯度滑降分割算法,利用多尺度形态学梯度,通过大小不同的结构元素提取图像梯度特征,获得梯度图像；然后利用滑降算法进行图像分割；同时,为了减少滑降算法的过分割现象,利用区域面积和区域相似性规则进行区域合并。另一方面将反馈脉冲耦合神经网络与滑降分割相结合,提出了一种新的MRI图像分割的特征提取算法。
Image Segmentation is a basic technique of image processing, and it is always one of the most difficult techniques in image processing and computer vision. In recent years, the researchers pay more and more attention to the interactive segmentation. On the base of arranging, summarizing and concluding interactive segmentation algorithm, some key theories and approaches in interactive segmentation and its applications are researched in this dissertation. In this dissertation, we attempt to have an in-depth investigation on the Live Wire algorithm, random walk and toboggan method. The main work is as follows:
Considering the speed disadvantage of the traditional Live Wire algorithm, a novel Live Wire algorithm based on Pusle Coupling Neural Network (PCNN) is proposed. PCNN is used to obtain the shortest path between the two points by user. The speed of operation is increased by proposed method, especially when dealing with a larger image, the rate reflected a clear advantage.
On the study of detection the footprint heel impression, this paper presents an improved Live Wire algorithm by considering the feature of weak edge for heel impression. Ant colony optimization (ACO) algorithm is used to find the shortest path. A new cost function is defined by Sobel operator. The least square method was used for ellipse fitting to obtain the parameters. The improved methods can effectively extract the information of the footprint heel impression.
In order to improve the efficiency and speed of random walk algorithm, a new random walk method based on toboggan is presented. Firstly, a graph is created which decomposes the image in scale and space using the concept of toboggan. In this way, we consider each of the regions as the nodes, the weights between graph-nodes is estimated by using the law of universal gravity. Then the label for object and background is drawn by user. Finally, this paper uses the theory of random walk algorithm to segment the image.
According to the multi-vehicle problem in Intelligence Transportation System, a vehicle detection algorithm is presented based on the combination of edge feature and random walk techniques. We used background subtraction and edge detection to obtain the moving area, then used morphological operations for vehicle skeleton extraction and get the seeds for random walk. Finally the accurate boundary of moving vehicles is detection by random walk.
Considering the over-segmentaion of the toboggan algorithm, two improve toboggan algorithms are proposed in this thesis. Firstly, a new toboggan is presented based on multi-scale morphological gradient. The gradient image is computed by using the multi-scale morphological gradient operation. It was obtained through the difference size of the structure elements from images gradient features. In order to reduce the over-segmentation of toboggan, the approach of region combination is used after toboggan segmentation. Secondly, combining the feedback pulse coupling neural network (FPCNN) with toboggan segmentation algorithm, this thesis presents a new MRI image segmentation feature extraction methods.
引文
1. GShapiro L, C.Stockman G.计算机视觉[M].北京：机械工业出版社,2005.
2. Gonzalez RC, Woods RE数字图像处理(第二版)[M].北京：电子工业出版社,2007.
3. Milan Sonka, Vaclav Hlavac, Roger Boyle著,艾海舟,武勃等译.图像处理、分析与机器视觉[M].北京：人民邮电出版社,2003.
4.章毓晋.图像分割[M].北京：科学出版社,2001.
5.荆仁杰,等.计算机图像处理[M].杭州：浙江大学出版社,1990.
6.朱秀昌,等.数字图像通信[M].北京：人民邮电出版社,1994.
7. Wenbing Tao, Hai Jin, Yimin Zhang, Liman Liu, Desheng Wang. Image Thresholding Using Graph Cuts[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A, Sept.2008 Vol.38, No.5 pp:1181-1195.
8. Pundlik, S.J. Woodard, D.L. Birchfield, S.T. Non-ideal iris segmentation using graph cuts[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2008. June 2008, pp:1-6.
9. Xiaoqing Liu, Veksler, O., Samarabandu, J. Graph cut with ordering constraints on labels and its applications[C]. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. June 2008, pp:1-8.
10. Xiaoyu Wu, Yangsheng Wang. Interactive Foreground/Background Segmentation Based on Graph Cut[C]. Congress on Image and Signal Processing,2008. CISP'08. May 2008, Vol.3, pp: 692-696.
11. Houhou, N., Thiran, J.-P., Bresson, X. Fast texture segmentation model based on the shape operator and active contour[C]. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. June 2008, pp:1-8.
12. Hongda Mao, Huafeng Liu, Pengcheng Shi. Neighbor-constrained active contours without edges[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2008. June 2008, pp:1-7.
13. Xianghua Xie, Mirmehdi, M. MAC:Magnetostatic Active Contour Model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008, Vol.30, No.4, pp:632-646.
14. Melonakos, J., Pichon, E., Angenent, S. Tannenbaum, A. Finsler Active Contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008 Vol.30, No.3 pp: 412-423.
15. Sum K.W., Cheung, P.Y.S. Vessel Extraction Under Non-Uniform Illumination:A Level Set Approach[J]. IEEE Transactions on Biomedical Engineering, Jan.2008, Vol.55 No.1, pp:358- 360.
16. Sheng Zheng, Chang-Cai Yang, Shi-Ling Xiang, Jin Ye. Makers based level set method for image segmentation[C].2008 International Conference on Machine Learning and Cybernetics, July 2008, Vol.2, pp:947-952.
17. Xian Fan Bazin, P.-L. Prince, J.L. A multi-compartment segmentation framework with homeomorphic level sets[C]. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. June 2008, pp:1-6.
18. Abdelmunim, H. Farag, A.A. Miller, W. AboelGhar, M. A kidney segmentation approach from DCE-MRI using level sets[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2008. June 2008, pp:1-6.
19. Sinop, A.K., Grady, L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm[C]. IEEE 11th International Conference on Computer Vision,2007, pp:1-8.
20. Kass, M. and Witkin, A., Terzopolous, D. Snakes:Active Contour Models[J]. International Journal of Computer Vision,1987, Vol.1, No.4, pp:321-331.
21. Osher S, Sethian J A. Fronts p ropagating with curvature dependent speed:algorithms based on Hamilton2Jacobi formulations [J]. Journal of Computational Physics,1988, Vol.79, No.1, pp: 12-4.
22. Mortensen E N, Morse B S, W A Barrett, J K Udupa. Adaptive Boundary Detection Using 'Live-Wire'Two-Dimensional Dynamic Programming[C]. In IEEE Proceedings of Computers in Cardiology, USA:Durham, October 1992,pp:635-638.
23. Boykov Yuri, Veksler Olga, Zabih Ramin. Fast approximate energy minimization via graph cuts[J]. IEEE Transactions on Pattern Analysis Machine Intelligence,2001, Vol.23, No.11, pp:1222-1239.
24. Grady L., Funka-Lea G. Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials[C], in Proc. Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis Workshop,2004, pp:230-245.
25.田捷,包尚联,周明全.医学影像处理与分析[M].北京：电子工业出版社,2003.
26. Mortensen E. N, Barrett W. A. Intelligent scissors for image composition[C]. Proceedings of Computer graphics and interactive techniques, August,1995, pp:191-198.
27. Kenneth M., Hanson. Medical Imaging:Image Processing[M], April 1997, Vol.3034, pp:228-235.
28. Udupa, J.K., Miyazawa, F.K. An ultra-fast user-steered image segmentation paradigm:live wire on the fly[J]. IEEE Transactions on Medical Imaging,2000, Vol.19, No.1, pp:55-62
29. Hongbing Ji. An interactive segmentation method for medical images[C]. The 6th International Conference on Signal Processing,2002, Vol.1, pp:26-30.
30. Barrett, W.A., Reese, L.J.,Mortensen, E.N. Intelligent segmentation tools[C]. IEEE International Symposium on Biomedical Imaging, July 2002, Vol.7-10, pp:217-220.
31. Fu-Ping Zhu, Jie Tian, Xi-Ping Luo, Xing-Fei Ge. Medical image series segmentation using watershed transform and active contour model[C]. International Conference on Machine Learning and Cybernetics, Nov.2002, Vol.2, pp:865-870.
32. Olivier Gerard, Thomas Deschamps, Myriam Greff, and Laurent D. Cohen,Real-time Interactive Path Extraction with On-The-Fly Adaptation of the External Forces [C],7th European Conference on Computer Vision (ECCV'02), Copenhagen, Denmark, May 2002. pp:807-821.
33. Huiguang He, Jie Tianl, Yao Lin, Ke Lu. A New Interactive Segmentation Scheme Based on Fuzzy Affinity and Live-Wire[C]. Second International Conference on Fuzzy Systems and Knowledge Discovery, (FSKD 2005),2005, LNAI 3613, pp:436-443.
34. Chodorowski Artur, Mattsson Ulf, Morgan Langille and Ghassan Hamarneh, Color Lesion Boundary Detection Using Live Wire[C], Image Processing on Medical Imaging, Proc. of SPIE, Bellingham, WA,2005, Vol.5747, pp:1589-1596.
35. FARBER, M., EHRHARDT, J., HANDELS, H., Automatic Atlas-Based Contour Extraction of Anatomical Structures in Medical Images [J]. Computer Assisted Radiology and Surgery,2005, Vol.1281, pp:272-277.
36. Kass M., Witkin A., Terzopoulos D. Snakes:Active contour models[J]. International Journal of Computer Vision.1987, Vol.1, No.4, pp:321-331.
37. Caselles V, Catte E, Coll T., and Dibos F. A geometric model for active contours in image processing[J]. Numerische Mathematik,1993, Vol.66, pp:1-31.
38. Caselles V, Kimmel R., Sapiro G. Geodesic Active Contours[J]. International Journal of Computer Vision,1997, Vol.22, No. 1,pp:61-79.
39. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Conformal curvature ows: From phase transitions to active vision[J]. Archive for Rational Mechanics and Analysis,1996, Vol.134,No.3,pp:275-301.
40. Cohen L D. On active contour models and balloons [J]. CVGIP:Image Understanding,1991, Vol.53, No.2,pp:211-218.
41. Chenyang Xu, Prince Jerry L. Snakes, shapes, and gradient vector flow[J]. IEEE Transactions on Image Processing,1998, Vol.7, No.3, pp:359-369.
42. Amini A A, Tehran S, Weymouth T E. Using dynamic programming for minimizing the energy of active contours in the presence of hard constrains[C]. In Proceedings of The 2nd International Conference of Computer Vision,1988, pp:95-99.
43. Cham TJ, Cipolla R. Stereo coupled active contours[C]. In Grewe.Proceedings of the international conference on CVPR. IEEE Computer Society Press,1997, pp:1094-1099.
44. Menet S, Saint-Mar P, Medion G B. Snakes:implementation andapplication to stereo[C]. In: Fua P, Hansan A J eds Proceedings ofthe DARPA Image Understanding Workshop. Pittsburgh, Pennsy21anis:IEEE Computer Society Press,1990, pp:720-726.
45. Storvik G. A Bayesian approach to dynamic contours through stochastic sampling and simulated Annealing[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1994, Vol.16, No.10, pp:970-986.
46.曹远星,董育宁.蛇模型综述[J].信息与技术.2006,Vol.3,pp：113-116.
47.李培华,张田文.主动轮廓线模型(蛇模型)综述[J].软件学报.2000,Vol.11,No.6,pp：751-757.
48. Yuille A L, M allinan PW, Cohen D S. Feature extraction from faces using defo rmable temp lates[J]. International Journal on Computer V ision,1992, Vol.8, No.2, pp:133-144.
49. Staib L H, Duncan T S. Boundary. Finding with parametrically defo rmable models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992, Vol.14, No.11, pp:1061-1075.
50. Lai K. F., Chin R. T. Deformable contours:modeling and extraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1995, Vol.17, No.11, pp:1084-1090.
51. Jolly M. P., Lak shmanan S, Jain A K. Vechile segmentation and classification using deformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996, Vol.18, No.3, pp:293-308.
52.周彦博,张广志.可变形物体的轮廓的提取[J].电子学报,1998.Vol.26,No.7,pp：133-137.
53.贾春光,谭鸥,段会龙等.基于变形轮廓的医学图像匹配方法[J].计算机辅助设计与图形学学报,1999,Vol.11,No.2,pp：115-119.
54. Szelisk i R, Terzopoulo s D. Physically Based and probalistic modeling for computer vision[C]. In:V emuri B C ed. P roceedings SPIE 1570, Geometric Methods in Computer Vision. San D iego, CA:Society of Photo Optical Instrumentation Engineers,1991, pp:140-152.
55. Osher S, Sethian J A. Fronts propagating with curvature-ependent speed:algorithms based on Hamilton-Jacobi formulations[J]. Journal of Computational Physics,1988, Vol.79, No.1, pp:12-49.
56. Malladi R, Sethian JA, Vermuri BC. Shape modeling with front propagation:a level set approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1995, Vol.17, No.2, pp:158-75.
57. Caselles, V, Kimmel, R., Sapiro, G. Geodesic active contours[C]. The Fifth International Conference on Computer Vision,1995, pp:694-699.
58. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A. Gradient flows and geometric active contour models[C]. The Fifth International Conference on Computer Vision,1995, pp:810-815.
59. Paragios, N., Mellina-Gottardo, O., Ramesh, V. Gradient vector flow fast geodesic active contours[C]. The Eighth IEEE International Conference on Computer Vision,2001, Vol.1, pp:67-73.
60. Yezzi, A., Tsai, A., Willsky, A. A statistical approach to image segmentation for bimodal and trimodal imagery[C]. The Proceedings of the Seventh IEEE International Conference on Computer Vision,1999, Vol.2, pp:898-903.
61. Paragios N., Deriche R. eodesic active regions for supervised texture segmentation[C]. The Proceedings of the Seventh IEEE International Conference onComputer Vision,1999, Vol.2, pp: 26-932.
62. Chan T, Vese L. Active contours without edges [J]. IEEE Transactions on Image Processing,2001, Vol.10,No.2,pp:266-277.
63.李俊,杨新,施鹏飞.基于Mumford2Shah模型的快速水平集图像分割方法[J].计算机学报,2002,Vol.25,No.11,pp：1175-1183.
64.朱付平,田捷,林瑶,葛行飞.基于Level Set方法的医学图像分割[J].软件学报.Vol.13,No.9,pp：1866-1872.
65.钱芸,张英杰.水平集的图像分割方法综述[J].中国图象图形学报.2008,Vol.13,No.1,pp：7-13.
66.闫成新,桑农,张天序.基于图论的图像分割研究进展[J].计算机工程与应用,2006,Vol.5,pp：11-14.
67. Shi J., Malik,J. Normalized cuts and image segmentation[C].In Processing IEEE Conference on Computer Vision and Pattern Recognition,1997, Vol.22, No.8, pp:731--737.
68. Chris Ding, Xiaofeng He, Hongyuan Zha. Spectral Min-max Cut for Graph Partitioning and Data Clustering[C]. Proceedings of the First IEEE International Conference on Data Mining,2001, pp:107-114.
69. Wu Z., Leahy R., An optimal graph theoretic approach to data clustering:theory and its application to image segmentation[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1993, Vol.15, No.11, pp:1101-1113.
70. Hongyuan Zha, Chris Ding, Ming Gu, Xiaofeng He and Horst Simon. Spectral Relaxation for K-means Clustering[C]. Advances in Neural Information Processing Systems 14, MIT Press(2002),2002, pp:1057-1064.
71.方贤勇,图像拼接技术研究[D].杭州：浙江大学,2005,7,pp：46-47.
72. Ford L., Fulkerson D. Flows in Networks [M], Princeton University Perss,1962. pp:102-132.
73. Boykov Y., Kolmogorov V., An Experimental ComParison of Min-Cut/Max-Flow Algorithms of Energy Minimization in Vision[J]. IEEE Transactions on Patterm Analysis and Machine Intelligence,2004, Vol.26, No.9, pp:1124-1137.
74. Boykov Y., Veksler O. Fast Approximate Energy Minimization via Graph Cuts[C]. Intemational Conefrence on Computer Vision,1999, pp:377-384.
75. Boykov,Y., Jolly,M.P. Interactive graph cuts foroptimal boundary®ion segmentation of objects in n-d images[C]. In Proceedings of Intemational Conefrence on Computer Vision,2001, Vol.1, pp:105-112.
76. Yin Li, Jian Sun, Chi-Keung Tang, and Heung-Yeung Shum. Lazy snapping [J]. ACM Transactionon Graphics,2004, Vol.23, No.3, pp:303-308.
77. Rother,C., Blake,A.,and Kolmogorov,V. Grabcut-Interactive foreground extraction using iterated graph cuts[J]. ACM Transaction on Graphics,2004, Vol.23, No.3, pp:309-314.
78.章卫祥,周秉锋.一种改进的Graph Cuts交互图像分割方法[J].影像技术,2007,Vol.4,pp：22-24.
79. Ning Xu, Bansal, R., Ahuja, N. Segmentation using graph cuts based active contours[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2,2003, pp:Ⅱ-46-53.
80. Kolmogorov, V., Rother, C. Minimizing Nonsubmodular Functions with Graph Cuts-A Review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, July 2007, Vol.29, NO.7, pp:1274-1279.
81. Marshall F. Tappen William T. Freeman. Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters[C]. Ninth IEEE International Conference on Computer Vision,2003, Vol.2, pp:900-906.
82. Veksler, O. Extracting dense features for visual correspondence with graph cuts[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003, Vol.1, pp:Ⅰ 689-694
83. Chia, A., Zagorodnov, V. Graph cut based segmentation of convoluted objects[C]. IEEE International Conference on Image Processing, ICIP 2005. Vol.3,11-14, pp:Ⅲ-848-51.
84. Slabaugh, G., Unal, G. Graph cuts segmentation using an elliptical shape prior[C]. IEEE International Conference on Image Processing, ICIP 2005. Vol.2,11-14, pp:Ⅱ-1222-5.
85. Sumengen, B., Bertelli, L., Manjunath, B.S. Fast and Adaptive Pairwise Similarities for Graph Cuts-based Image Segmentation[C]. Conference on Computer Vision and Pattern Recognition Workshop, June 2006,17-22, pp:179-179.
86. Huy-Nam Doan, Slabaugh, G, Unal, G, Tong Fang. Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts[C]. IEEE International Conference on Image Processing,2006 Oct.2006,8-11 pp:1913-1916.
87. Kohli, P., Torr, P.H.S. Dynamic Graph Cuts for Efficient Inference in Markov Random Fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007, Vo.29, No.12, pp:2079-2088.
88. Pundlik, S.J., Woodard, D.L., Birchfield, S.T. Non-ideal iris segmentation using graph cuts[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2008,23-28, pp:1-6.
89. Nhat Vu, Manjunath, B.S., Shape prior segmentation of multiple objects with graph cuts[C]. IEEE Conference Computer Vision and Pattern Recognition, CVPR 2008, pp:1-8.
90.马明祥.复杂背景下结合Graph Cut分割的车牌定位算法[J].科技信息,2008,No.16,pp：382.
91.邹昆,韩国强,李闻,张潇元.基于Graph Cut的快速纹理合成算法[J].计算机辅助设计与图形学学报,2008,Vol.120,No.15,pp：652-658.
92.吴亚东,孙世新,张红英,韩永国,陈波.一种基于图割的全变差图像去噪算法[J].电子学报,2007,Vo1.35,No.2,pp：265-268.
93.侯叶,郭宝龙.基于图切割的人体运动检测[J].光电子·激光.2007,Vo1.18,No.6,pp：725-728.
94.徐利娜,彭国华.图像分割中一种改进的图割模型[J].科学技术与工程,2006,Vol.6,No.18,pp：2583-2857.
95. Marina Maila and Jianbo Shi, a random walks view of spectral segmentation [J]. AI and STATISTICS 2001,(http://www.stat.washington.edu/mmp/Papers/aistats2001.ps)
96. Marina Maila, Jianbo Shi. Learning Segmentation with Random Walk[C]. NIPS,2001, (http://www.stat.washington.edu/mmp/Papers/nips2000.ps)
97. Grady L., Funka-Lea G. Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials[C]. In Computer Vision and MathematicalMethods in Medical and Biomedical Image Analysis, ECCV 2004, number LNCS3117, pp:230-245.
98. Grady L, Schiwietz T, Aharon S, et al. Random walks for interactive organ segmentation in two and three dimensions:Implementation and validation[C].8th International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI.2005,3750, pp:773-80.
99. Grady L. Random Walks for Image Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, November 2006, Vol.28, No.11, pp:1768-1783.
100. Grady, L., Sinop, A.K. Fast approximate Random Walker segmentation using eigenvector precomputation[C].2008 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), June 2008,23-28, pp:1-8.
101. Singaraju, D., Grady, L., Vidal, R. Interactive image segmentation via minimization of quadratic energies on directed graphs[C].2008 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), June 2008,23-28, pp:1-8.
102.简江涛.形变模型技术研究及其在医学图像分割中的应用[D].合肥：中国科学技术大学,2006,10,pp：2-3.
103. Dunena J.S., Ayaehe N.. Medieal Image Analysis:Progress over Two Decades and the Challenges Ahead[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,2000, Vol.22, No.1,pp:181-204.
104.翁璇,郑小林,姜海,医学图像分割技术研究进展[J].医疗卫生装备.2007,Vol.28,No.1,pp：37-39.
105.李楠,张为.医学图像分割技术综述[J].阴山学刊,2007,Vol.21 No.1,pp：35-36.
106.罗希平,田捷,林瑶.一种基于主动轮廓模型的医学图像序列分割算法[J].软件学报.2002,Vol.13,No.6,pp：1050-1058.
107.赵大哲,杨金柱,徐心和.一种基于多层CT影像的肺部结节分割方法[J].电子学报,2006,Vol.34,No.12,pp：478-2480.
108.徐伟,陈新,李鑫.一种,快速有效的交互式医学序列图像分割方法[J].现代电子技术,2007,No.16,pp：124-127.
109.王阳萍,党建武,李强,李莎.基于改进搜索策略的Live Wire |医学图像分割算法[J].计算机工程与应用,2007,Vol.43,No.29,pp：24-26.
110.纪其进.一种基于脉冲耦合神经网络的最短路径算法[J].小型微型计算机系统,2005,Vol.26,No.5,pp：826-829.
111. John H Caulfield, Jason MKinser. Finding shortest path in the shortest time using PCNN's[J]. IEEE Transaction on Neural Networks,1999, Vol.10, No.3, pp:604-606.
112.顾晓东,余道衡,张立明.时延PCNN及其用于求解最短路径[J].电子学报,2004,Vol.32,No.9,pp：1441-1443.
113.Z.Yi, K.K.Tan. Dynamic stability conditions for Lotka Volterra recurrent neural networks with delays [J], Physical Review,2002, Vol.11, No.4, pp:320-327.
114. Johnson J. L. and Padgett M. L. PCNN models and applications [J]. IEEE Transactions on Neural Networks,1999, Vol.10, No.3, pp:480-498.
115.马义德,齐春亮.基于遗传算法的脉冲耦合神经网络自动系统的研究[J].系统仿真学报.2006,Vol.18,No.3,pp：722-725.
116. X.D. Gu, D.H. Yu, L.M. Zhang, Global icons and local icons of images based Unit-linking PCNN and their application to robot navigation[J]. Lecture Notes In Computer Science,3497,2005, pp: 836-841.
117.孔祥维,黄静,石浩.基于改进的脉冲耦合神经网络的红外目标分割方法[J].红外与毫米波学报,2001,Vol.20,No.5,pp：365-369.
118. X.D. Gu, D. H. Yu, L. M. Zhang. Image shadow removal using pulse coupled neural network[J]. IEEE Transaction Neural Networks, May 2005, Vol.16, No.3, pp:692-698.
119. Zhan F B. Three Fastest Shortest Path Algorithms on Real Road Networks Journal of Geograph [J]. Information and Decision Analysis,1997, Vol.1, No.1 pp:69-82.
120. Mortensen EN, Barrett WA. Intelligent scissors for image composition[C]. Computer Graphics SIGGRAPH'95 Proceedings. Los Angeles, CA, ACM SIGGRAPH,1995, pp:191-198.
121. Mortensen Eric N, Barrett William A. Interactive Segmentation with Intelligent Scissors [J]. Graphical Models and Image Processing.1998, Vol.60, No.5, pp:349-384.
122. Eckhorn R, Reitboeck H J, Arndt M, Dicke P. Feature liking via synchronization among distributed assemblies:Simulations of results from cat visual cortex [J]. Neural Computation. 1990, Vol.2, No.3, pp:293-307.
123.吴旭芒,高以群.足迹学[M].北京：警官教育出版社,1996.
124.李磊,童莉,平西建.平面赤足迹的形状分析[J].计算机辅助设计与图形学学报,2006,Vol.18,No.7,pp：976-981.
125.张涛,平西建,邵美珍.自回归模型在平面曲线识别中的应用[J].计算机研究与发展,Vol.37,No.8,2000,pp：942-947.
126.童莉,平西建,马金全,李磊.基于DFD参数变形模型的平面足迹轮廓提取[J].计算机辅助设计与图形学学报,2007,Vol·19,No·4,pp：521-527
127.黄群.赤脚足迹的统计分析[J].辽宁警专学报,2005,No.1,pp：5-9.
128.王卫东,平西建,丁益洪.立体足迹重压面提取与描述[J].微算计机信息,2005,Vol.21,No.11.pp：157-159.
129.王清举,平西建,王永栋.立体足迹计算机自动识别系统的应用[J].刑事技术,2003,No.5,pp：41-43.
130.雷航,童莉,平西建.平面赤足迹特征分析与身份识别方法[J].计算机辅助设计与图形学学报,2008,Vol.20,No.5,pp：659-664.
131.高以群,史力民.足迹检验图谱[M].北京：警官教育出版社,1997.
132.张强,王坤,郭丽,高立群.基于改进GVF和最小二乘法的弱边界椭圆提取[J].计算机应用,2007,Vol.27,No.4,pp：979-98.
133.黄贵玲,高西全,靳松杰,谈飞洋.基于蚁群算法的最短路径问题的研究和应用[J].计算机工程与应用,2007,Vol.43,No.13,pp：233-235.
134. Dorigo M., Gambardella LM. AntColonies for Traveling Salesman Problem[J]. BioSystems, 1997, Vol.43, pp:73-81.
135. Ying-Tung Hsiao, Cheng-Long Chnang, Cheng-Chih Chien. Ant Colony Optimization for Best Path Planning[C]. IEEE International Symposium on Communications and Information Technology, (ISCIT 2004), Oct.2004, Vol.1, pp:109-113.
136. Fitzgibbon AW, PiluM, Fischer R B. Direct least squares fitting of ellipses[C]. Proceedings of the 13th International Conference on Pattern Recognition Vienna,1996, pp:253-257.
137. Zhang, G, Jayas, D.S., and White, N.D.G. Separation of Touching Grain Kernels in an Image by Ellipse Fitting Algorithm[J]. Biosystems Engineering,2005, Vol.92, No.2, pp:135-142.
138.廖志军.交互式图像分割算法研究[D].北京：中国科学院计算机技术研究所,2006,6,pp：9-11.
139. A.Blake, C.Rother, M.Brown, P.Perez, P. Torr. Interactive Image Segmentation using an Adaptive GMMRF Model[C].In Proceeding European Conference on Computer Vision, (ECCV 2004, LNCS3021),pp:428-441.
140. Doyle P., Snell L.. Random walks and electric networks[M]. Number 22 in Carus mathematical monographs. Mathematical Association of America, Washington, D.C.,1984.
141. Fairfield, J., Toboggan contrast enhancement for contrast segmentation[C]. Proceedings of 10th International Conference on Pattern Recognition, June 1990, Vol.1,16-21, pp:712-716.
142. Yung-Chieh Lin, Yu-Pao Tsai, Yi-Ping Hung, Zen-Chung Shih. Comparison Between Immersion-Based and Toboggan-Based Watershed Image Segmentation[J]. IEEE Transactions on Image Processing, March 2006, Vol.15, No.3, pp:632-640.
143. Sinop, A. K., Grady, L. A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm. IEEE 11th International Conference on Computer Vision, (ICCV 2007),14-21 Oct.007, pp:1-8.
144. Genyun Sun, Qinhuo Liu, Qiang Liu, Changyuan Ji, Xiaowen Li. A novel approach for edge detection based on the theory of universal gravity[J]. Pattern Recognition, October 2007, Vol.40, No.10, pp:2766-2775.
145. Cheng H D,Sun Y.A Hierarchical Approach to Color Image Segmentation Using Homogeneity[J].IEEE Transaction on Image Processing,2000,Vol.9, No.12, pp:2071-2082.
146.张伟.基于视觉的运动车辆检测与跟踪[D].上海：上海交通大学,2007,pp：6.
147.赵建云,郑晓势,周伟,刘广起.基于视频的车辆检测与跟踪技术综述[J].计算机与信息技术,2007,No.4,pp：30-32.
148.胡铟,杨静宇.基于单目视觉的路面车辆检测及跟踪方法综述[J].公路交通科技.2007,Vol.24,No.12,pp：127-131.
149.朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪[J].指挥控制与仿真,2006,Vol.30 No.3,pp：1004-1009
150.刘宏,李锦涛,刘群.融合颜色和梯度特征的运动阴影消除方法[J].计算机辅助设计与图形学学报,Oct.2007,Vol.19,No.10,pp：1279-1285.
151. Malcolm, J., Rathi, Y., Tannenbaum, A. Multi-Object Tracking Through Clutter Using Graph Cuts[C]. IEEE 11th International Conference on Computer Vision. (ICCV 2007),14-21 Oct. 2007, pp:1-5.
152.侯叶,郭宝龙.基于图论的运动对象分割[J].吉林大学学报(工学版).2008,Vol.38,No.4,pp：902-906.
153.Wieclawek, W., Pietka, E. Live-Wire-Based 3D Segmentation MethodfC]. The 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, (EMBS 2007),22-26, Aug.2007, pp:5645-5648.
154. Sundaramoorthi, G, Yezzi, A., Mennucci, A. C. Coarse-to-Fine Segmentation and Tracking Using Sobolev Active Contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2008, Vol.30, No.5, pp:851-864.
155. Haritaoglu I, Harwood D, Davis L. W4:Real time surveillance of peop le and their activities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, Vol.22, No.8, pp: 809-830.
156. Pfaltz J L, Rosenfeld A. Computer Representation of Planar Regions by Their Skeletons[J] Communications of ACM,1967, Vol.10, No.2, pp:119-122.
157.王华伟,李翠华,施华,韦凤梅.基于HSV空间和一阶梯度的阴影剪除算法[J],计算机工程与应用,2005,No.8,pp：43-45.
158.吴成东,郭利锋,张云洲,刘濛,多车辆跟踪时目标粘连的解决方法[J].东北大学学报(自然科学版)2008,Vol.129,No.8,pp：1065-1068.
159. Fairfield, John. Toboggan contrast enhancement[C]. Proceedings of SPIE-The International Society for Optical Engineering,1992, Vol.1708, pp:282-292.
160. Yao Xu, Hung, Yi-Ping. Fast image segmentation by sliding in the derivative terrain [C]. Proceedings of SPIE-The International Society for Optical Engineering,1992,Vol.1607, pp: 369-37.
161. Ibrahim, H., Petrou, M., Wells, K., Doran, S., Olsen, O. Preprocessing for use in automatic volumetric liver segmentation from NMR Data[C].2004 IEEE Nuclear Science Symposium Conference Record (IEEE Cat. No.04CH37604),2004, Vol.5, pp:2783-7.
162. Armstrong, C.J. Price, B.L., Barrett, W.A. Interactive segmentation of image volumes with Live Surface[J]. Computers & Graphics, April 2007, Vol.31, No.2, pp:212-29.
163. Mortensen E. N., Barrett W. A., Toboggan-based intelligent scissors with a four-parameter edge model[C]. IEEE Conferrence on Computer Vision and Pattern Recognition,1999, Vol.2, pp: 452-458.
164. Wang D.A. multiscale gradient algorithm for image segmentation using watershed[J]. Pattern Recognition,1997,301984, Vol.6, No.6, pp:661-674.
165. HarisK, Efstratiadis SerafimN, MaglaverasNicos. Hbrid image segmentation using watersheds and fast region mering[J]. IEEE Transactions on Image Processing,1998, Vol.2, pp:1684-1699.
166. Vincent L, Soille P. Watersheds in digital space:an efficient algorithm based on immersion simulations[J]. IEEE Transaction on Pattern. Analysis and Machine Intelligence.1991, Vol.13, No.6, pp:583-598.
167.卢官明.一种计算图像形态梯度的多尺度算法[J].中国图象图形学报,2001,Vol.6,No.3,pp：214-218.
168. Eckhorn R., Reitboeck H.J., Dicke M.A., P. Feature linking via synchronization among distributed assemblies:simulation of results from cat cortex[J]. Neural Computing.1990, Vol.2, pp:293-307.
169. Lindbald, T., Kinser, J.M.:Image processing using pulse-coupled neural networks[M]. Springer, 1998.
170. Yiyan Xue, Simon X. Yang. Image Segmentation Using Watershed Transform and Feed-Back Pulse Coupled Neural Network[J]. Lecture Notes in Computer Science, 2005, Vol.3696, pp:531-536.
171. Kinser, J.M., Johnson, J.L. Stabilized input with a feedback pulse-coupled neural network[J]. Optical Engineering.1996, Vol.35, pp:2158-2161.
172. Kuntimad, G., Ranganath, H. Perfect image segmentation using pulse coupled neural networks[J]. IEEE Transaction on Neural Networks,1999, Vol.10, pp:591-598.
2. Gonzalez RC, Woods RE数字图像处理(第二版)[M].北京：电子工业出版社,2007.
3. Milan Sonka, Vaclav Hlavac, Roger Boyle著,艾海舟,武勃等译.图像处理、分析与机器视觉[M].北京：人民邮电出版社,2003.
4.章毓晋.图像分割[M].北京：科学出版社,2001.
5.荆仁杰,等.计算机图像处理[M].杭州：浙江大学出版社,1990.
6.朱秀昌,等.数字图像通信[M].北京：人民邮电出版社,1994.
7. Wenbing Tao, Hai Jin, Yimin Zhang, Liman Liu, Desheng Wang. Image Thresholding Using Graph Cuts[J]. IEEE Transactions on Systems, Man and Cybernetics, Part A, Sept.2008 Vol.38, No.5 pp:1181-1195.
8. Pundlik, S.J. Woodard, D.L. Birchfield, S.T. Non-ideal iris segmentation using graph cuts[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2008. June 2008, pp:1-6.
9. Xiaoqing Liu, Veksler, O., Samarabandu, J. Graph cut with ordering constraints on labels and its applications[C]. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. June 2008, pp:1-8.
10. Xiaoyu Wu, Yangsheng Wang. Interactive Foreground/Background Segmentation Based on Graph Cut[C]. Congress on Image and Signal Processing,2008. CISP'08. May 2008, Vol.3, pp: 692-696.
11. Houhou, N., Thiran, J.-P., Bresson, X. Fast texture segmentation model based on the shape operator and active contour[C]. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. June 2008, pp:1-8.
12. Hongda Mao, Huafeng Liu, Pengcheng Shi. Neighbor-constrained active contours without edges[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2008. June 2008, pp:1-7.
13. Xianghua Xie, Mirmehdi, M. MAC:Magnetostatic Active Contour Model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2008, Vol.30, No.4, pp:632-646.
14. Melonakos, J., Pichon, E., Angenent, S. Tannenbaum, A. Finsler Active Contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008 Vol.30, No.3 pp: 412-423.
15. Sum K.W., Cheung, P.Y.S. Vessel Extraction Under Non-Uniform Illumination:A Level Set Approach[J]. IEEE Transactions on Biomedical Engineering, Jan.2008, Vol.55 No.1, pp:358- 360.
16. Sheng Zheng, Chang-Cai Yang, Shi-Ling Xiang, Jin Ye. Makers based level set method for image segmentation[C].2008 International Conference on Machine Learning and Cybernetics, July 2008, Vol.2, pp:947-952.
17. Xian Fan Bazin, P.-L. Prince, J.L. A multi-compartment segmentation framework with homeomorphic level sets[C]. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. June 2008, pp:1-6.
18. Abdelmunim, H. Farag, A.A. Miller, W. AboelGhar, M. A kidney segmentation approach from DCE-MRI using level sets[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2008. June 2008, pp:1-6.
19. Sinop, A.K., Grady, L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm[C]. IEEE 11th International Conference on Computer Vision,2007, pp:1-8.
20. Kass, M. and Witkin, A., Terzopolous, D. Snakes:Active Contour Models[J]. International Journal of Computer Vision,1987, Vol.1, No.4, pp:321-331.
21. Osher S, Sethian J A. Fronts p ropagating with curvature dependent speed:algorithms based on Hamilton2Jacobi formulations [J]. Journal of Computational Physics,1988, Vol.79, No.1, pp: 12-4.
22. Mortensen E N, Morse B S, W A Barrett, J K Udupa. Adaptive Boundary Detection Using 'Live-Wire'Two-Dimensional Dynamic Programming[C]. In IEEE Proceedings of Computers in Cardiology, USA:Durham, October 1992,pp:635-638.
23. Boykov Yuri, Veksler Olga, Zabih Ramin. Fast approximate energy minimization via graph cuts[J]. IEEE Transactions on Pattern Analysis Machine Intelligence,2001, Vol.23, No.11, pp:1222-1239.
24. Grady L., Funka-Lea G. Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials[C], in Proc. Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis Workshop,2004, pp:230-245.
25.田捷,包尚联,周明全.医学影像处理与分析[M].北京：电子工业出版社,2003.
26. Mortensen E. N, Barrett W. A. Intelligent scissors for image composition[C]. Proceedings of Computer graphics and interactive techniques, August,1995, pp:191-198.
27. Kenneth M., Hanson. Medical Imaging:Image Processing[M], April 1997, Vol.3034, pp:228-235.
28. Udupa, J.K., Miyazawa, F.K. An ultra-fast user-steered image segmentation paradigm:live wire on the fly[J]. IEEE Transactions on Medical Imaging,2000, Vol.19, No.1, pp:55-62
29. Hongbing Ji. An interactive segmentation method for medical images[C]. The 6th International Conference on Signal Processing,2002, Vol.1, pp:26-30.
30. Barrett, W.A., Reese, L.J.,Mortensen, E.N. Intelligent segmentation tools[C]. IEEE International Symposium on Biomedical Imaging, July 2002, Vol.7-10, pp:217-220.
31. Fu-Ping Zhu, Jie Tian, Xi-Ping Luo, Xing-Fei Ge. Medical image series segmentation using watershed transform and active contour model[C]. International Conference on Machine Learning and Cybernetics, Nov.2002, Vol.2, pp:865-870.
32. Olivier Gerard, Thomas Deschamps, Myriam Greff, and Laurent D. Cohen,Real-time Interactive Path Extraction with On-The-Fly Adaptation of the External Forces [C],7th European Conference on Computer Vision (ECCV'02), Copenhagen, Denmark, May 2002. pp:807-821.
33. Huiguang He, Jie Tianl, Yao Lin, Ke Lu. A New Interactive Segmentation Scheme Based on Fuzzy Affinity and Live-Wire[C]. Second International Conference on Fuzzy Systems and Knowledge Discovery, (FSKD 2005),2005, LNAI 3613, pp:436-443.
34. Chodorowski Artur, Mattsson Ulf, Morgan Langille and Ghassan Hamarneh, Color Lesion Boundary Detection Using Live Wire[C], Image Processing on Medical Imaging, Proc. of SPIE, Bellingham, WA,2005, Vol.5747, pp:1589-1596.
35. FARBER, M., EHRHARDT, J., HANDELS, H., Automatic Atlas-Based Contour Extraction of Anatomical Structures in Medical Images [J]. Computer Assisted Radiology and Surgery,2005, Vol.1281, pp:272-277.
36. Kass M., Witkin A., Terzopoulos D. Snakes:Active contour models[J]. International Journal of Computer Vision.1987, Vol.1, No.4, pp:321-331.
37. Caselles V, Catte E, Coll T., and Dibos F. A geometric model for active contours in image processing[J]. Numerische Mathematik,1993, Vol.66, pp:1-31.
38. Caselles V, Kimmel R., Sapiro G. Geodesic Active Contours[J]. International Journal of Computer Vision,1997, Vol.22, No. 1,pp:61-79.
39. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Conformal curvature ows: From phase transitions to active vision[J]. Archive for Rational Mechanics and Analysis,1996, Vol.134,No.3,pp:275-301.
40. Cohen L D. On active contour models and balloons [J]. CVGIP:Image Understanding,1991, Vol.53, No.2,pp:211-218.
41. Chenyang Xu, Prince Jerry L. Snakes, shapes, and gradient vector flow[J]. IEEE Transactions on Image Processing,1998, Vol.7, No.3, pp:359-369.
42. Amini A A, Tehran S, Weymouth T E. Using dynamic programming for minimizing the energy of active contours in the presence of hard constrains[C]. In Proceedings of The 2nd International Conference of Computer Vision,1988, pp:95-99.
43. Cham TJ, Cipolla R. Stereo coupled active contours[C]. In Grewe.Proceedings of the international conference on CVPR. IEEE Computer Society Press,1997, pp:1094-1099.
44. Menet S, Saint-Mar P, Medion G B. Snakes:implementation andapplication to stereo[C]. In: Fua P, Hansan A J eds Proceedings ofthe DARPA Image Understanding Workshop. Pittsburgh, Pennsy21anis:IEEE Computer Society Press,1990, pp:720-726.
45. Storvik G. A Bayesian approach to dynamic contours through stochastic sampling and simulated Annealing[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1994, Vol.16, No.10, pp:970-986.
46.曹远星,董育宁.蛇模型综述[J].信息与技术.2006,Vol.3,pp：113-116.
47.李培华,张田文.主动轮廓线模型(蛇模型)综述[J].软件学报.2000,Vol.11,No.6,pp：751-757.
48. Yuille A L, M allinan PW, Cohen D S. Feature extraction from faces using defo rmable temp lates[J]. International Journal on Computer V ision,1992, Vol.8, No.2, pp:133-144.
49. Staib L H, Duncan T S. Boundary. Finding with parametrically defo rmable models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992, Vol.14, No.11, pp:1061-1075.
50. Lai K. F., Chin R. T. Deformable contours:modeling and extraction[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1995, Vol.17, No.11, pp:1084-1090.
51. Jolly M. P., Lak shmanan S, Jain A K. Vechile segmentation and classification using deformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996, Vol.18, No.3, pp:293-308.
52.周彦博,张广志.可变形物体的轮廓的提取[J].电子学报,1998.Vol.26,No.7,pp：133-137.
53.贾春光,谭鸥,段会龙等.基于变形轮廓的医学图像匹配方法[J].计算机辅助设计与图形学学报,1999,Vol.11,No.2,pp：115-119.
54. Szelisk i R, Terzopoulo s D. Physically Based and probalistic modeling for computer vision[C]. In:V emuri B C ed. P roceedings SPIE 1570, Geometric Methods in Computer Vision. San D iego, CA:Society of Photo Optical Instrumentation Engineers,1991, pp:140-152.
55. Osher S, Sethian J A. Fronts propagating with curvature-ependent speed:algorithms based on Hamilton-Jacobi formulations[J]. Journal of Computational Physics,1988, Vol.79, No.1, pp:12-49.
56. Malladi R, Sethian JA, Vermuri BC. Shape modeling with front propagation:a level set approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1995, Vol.17, No.2, pp:158-75.
57. Caselles, V, Kimmel, R., Sapiro, G. Geodesic active contours[C]. The Fifth International Conference on Computer Vision,1995, pp:694-699.
58. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A. Gradient flows and geometric active contour models[C]. The Fifth International Conference on Computer Vision,1995, pp:810-815.
59. Paragios, N., Mellina-Gottardo, O., Ramesh, V. Gradient vector flow fast geodesic active contours[C]. The Eighth IEEE International Conference on Computer Vision,2001, Vol.1, pp:67-73.
60. Yezzi, A., Tsai, A., Willsky, A. A statistical approach to image segmentation for bimodal and trimodal imagery[C]. The Proceedings of the Seventh IEEE International Conference on Computer Vision,1999, Vol.2, pp:898-903.
61. Paragios N., Deriche R. eodesic active regions for supervised texture segmentation[C]. The Proceedings of the Seventh IEEE International Conference onComputer Vision,1999, Vol.2, pp: 26-932.
62. Chan T, Vese L. Active contours without edges [J]. IEEE Transactions on Image Processing,2001, Vol.10,No.2,pp:266-277.
63.李俊,杨新,施鹏飞.基于Mumford2Shah模型的快速水平集图像分割方法[J].计算机学报,2002,Vol.25,No.11,pp：1175-1183.
64.朱付平,田捷,林瑶,葛行飞.基于Level Set方法的医学图像分割[J].软件学报.Vol.13,No.9,pp：1866-1872.
65.钱芸,张英杰.水平集的图像分割方法综述[J].中国图象图形学报.2008,Vol.13,No.1,pp：7-13.
66.闫成新,桑农,张天序.基于图论的图像分割研究进展[J].计算机工程与应用,2006,Vol.5,pp：11-14.
67. Shi J., Malik,J. Normalized cuts and image segmentation[C].In Processing IEEE Conference on Computer Vision and Pattern Recognition,1997, Vol.22, No.8, pp:731--737.
68. Chris Ding, Xiaofeng He, Hongyuan Zha. Spectral Min-max Cut for Graph Partitioning and Data Clustering[C]. Proceedings of the First IEEE International Conference on Data Mining,2001, pp:107-114.
69. Wu Z., Leahy R., An optimal graph theoretic approach to data clustering:theory and its application to image segmentation[J], IEEE Transactions on Pattern Analysis and Machine Intelligence,1993, Vol.15, No.11, pp:1101-1113.
70. Hongyuan Zha, Chris Ding, Ming Gu, Xiaofeng He and Horst Simon. Spectral Relaxation for K-means Clustering[C]. Advances in Neural Information Processing Systems 14, MIT Press(2002),2002, pp:1057-1064.
71.方贤勇,图像拼接技术研究[D].杭州：浙江大学,2005,7,pp：46-47.
72. Ford L., Fulkerson D. Flows in Networks [M], Princeton University Perss,1962. pp:102-132.
73. Boykov Y., Kolmogorov V., An Experimental ComParison of Min-Cut/Max-Flow Algorithms of Energy Minimization in Vision[J]. IEEE Transactions on Patterm Analysis and Machine Intelligence,2004, Vol.26, No.9, pp:1124-1137.
74. Boykov Y., Veksler O. Fast Approximate Energy Minimization via Graph Cuts[C]. Intemational Conefrence on Computer Vision,1999, pp:377-384.
75. Boykov,Y., Jolly,M.P. Interactive graph cuts foroptimal boundary®ion segmentation of objects in n-d images[C]. In Proceedings of Intemational Conefrence on Computer Vision,2001, Vol.1, pp:105-112.
76. Yin Li, Jian Sun, Chi-Keung Tang, and Heung-Yeung Shum. Lazy snapping [J]. ACM Transactionon Graphics,2004, Vol.23, No.3, pp:303-308.
77. Rother,C., Blake,A.,and Kolmogorov,V. Grabcut-Interactive foreground extraction using iterated graph cuts[J]. ACM Transaction on Graphics,2004, Vol.23, No.3, pp:309-314.
78.章卫祥,周秉锋.一种改进的Graph Cuts交互图像分割方法[J].影像技术,2007,Vol.4,pp：22-24.
79. Ning Xu, Bansal, R., Ahuja, N. Segmentation using graph cuts based active contours[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2,2003, pp:Ⅱ-46-53.
80. Kolmogorov, V., Rother, C. Minimizing Nonsubmodular Functions with Graph Cuts-A Review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, July 2007, Vol.29, NO.7, pp:1274-1279.
81. Marshall F. Tappen William T. Freeman. Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters[C]. Ninth IEEE International Conference on Computer Vision,2003, Vol.2, pp:900-906.
82. Veksler, O. Extracting dense features for visual correspondence with graph cuts[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003, Vol.1, pp:Ⅰ 689-694
83. Chia, A., Zagorodnov, V. Graph cut based segmentation of convoluted objects[C]. IEEE International Conference on Image Processing, ICIP 2005. Vol.3,11-14, pp:Ⅲ-848-51.
84. Slabaugh, G., Unal, G. Graph cuts segmentation using an elliptical shape prior[C]. IEEE International Conference on Image Processing, ICIP 2005. Vol.2,11-14, pp:Ⅱ-1222-5.
85. Sumengen, B., Bertelli, L., Manjunath, B.S. Fast and Adaptive Pairwise Similarities for Graph Cuts-based Image Segmentation[C]. Conference on Computer Vision and Pattern Recognition Workshop, June 2006,17-22, pp:179-179.
86. Huy-Nam Doan, Slabaugh, G, Unal, G, Tong Fang. Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts[C]. IEEE International Conference on Image Processing,2006 Oct.2006,8-11 pp:1913-1916.
87. Kohli, P., Torr, P.H.S. Dynamic Graph Cuts for Efficient Inference in Markov Random Fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2007, Vo.29, No.12, pp:2079-2088.
88. Pundlik, S.J., Woodard, D.L., Birchfield, S.T. Non-ideal iris segmentation using graph cuts[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2008,23-28, pp:1-6.
89. Nhat Vu, Manjunath, B.S., Shape prior segmentation of multiple objects with graph cuts[C]. IEEE Conference Computer Vision and Pattern Recognition, CVPR 2008, pp:1-8.
90.马明祥.复杂背景下结合Graph Cut分割的车牌定位算法[J].科技信息,2008,No.16,pp：382.
91.邹昆,韩国强,李闻,张潇元.基于Graph Cut的快速纹理合成算法[J].计算机辅助设计与图形学学报,2008,Vol.120,No.15,pp：652-658.
92.吴亚东,孙世新,张红英,韩永国,陈波.一种基于图割的全变差图像去噪算法[J].电子学报,2007,Vo1.35,No.2,pp：265-268.
93.侯叶,郭宝龙.基于图切割的人体运动检测[J].光电子·激光.2007,Vo1.18,No.6,pp：725-728.
94.徐利娜,彭国华.图像分割中一种改进的图割模型[J].科学技术与工程,2006,Vol.6,No.18,pp：2583-2857.
95. Marina Maila and Jianbo Shi, a random walks view of spectral segmentation [J]. AI and STATISTICS 2001,(http://www.stat.washington.edu/mmp/Papers/aistats2001.ps)
96. Marina Maila, Jianbo Shi. Learning Segmentation with Random Walk[C]. NIPS,2001, (http://www.stat.washington.edu/mmp/Papers/nips2000.ps)
97. Grady L., Funka-Lea G. Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials[C]. In Computer Vision and MathematicalMethods in Medical and Biomedical Image Analysis, ECCV 2004, number LNCS3117, pp:230-245.
98. Grady L, Schiwietz T, Aharon S, et al. Random walks for interactive organ segmentation in two and three dimensions:Implementation and validation[C].8th International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI.2005,3750, pp:773-80.
99. Grady L. Random Walks for Image Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, November 2006, Vol.28, No.11, pp:1768-1783.
100. Grady, L., Sinop, A.K. Fast approximate Random Walker segmentation using eigenvector precomputation[C].2008 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), June 2008,23-28, pp:1-8.
101. Singaraju, D., Grady, L., Vidal, R. Interactive image segmentation via minimization of quadratic energies on directed graphs[C].2008 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), June 2008,23-28, pp:1-8.
102.简江涛.形变模型技术研究及其在医学图像分割中的应用[D].合肥：中国科学技术大学,2006,10,pp：2-3.
103. Dunena J.S., Ayaehe N.. Medieal Image Analysis:Progress over Two Decades and the Challenges Ahead[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,2000, Vol.22, No.1,pp:181-204.
104.翁璇,郑小林,姜海,医学图像分割技术研究进展[J].医疗卫生装备.2007,Vol.28,No.1,pp：37-39.
105.李楠,张为.医学图像分割技术综述[J].阴山学刊,2007,Vol.21 No.1,pp：35-36.
106.罗希平,田捷,林瑶.一种基于主动轮廓模型的医学图像序列分割算法[J].软件学报.2002,Vol.13,No.6,pp：1050-1058.
107.赵大哲,杨金柱,徐心和.一种基于多层CT影像的肺部结节分割方法[J].电子学报,2006,Vol.34,No.12,pp：478-2480.
108.徐伟,陈新,李鑫.一种,快速有效的交互式医学序列图像分割方法[J].现代电子技术,2007,No.16,pp：124-127.
109.王阳萍,党建武,李强,李莎.基于改进搜索策略的Live Wire |医学图像分割算法[J].计算机工程与应用,2007,Vol.43,No.29,pp：24-26.
110.纪其进.一种基于脉冲耦合神经网络的最短路径算法[J].小型微型计算机系统,2005,Vol.26,No.5,pp：826-829.
111. John H Caulfield, Jason MKinser. Finding shortest path in the shortest time using PCNN's[J]. IEEE Transaction on Neural Networks,1999, Vol.10, No.3, pp:604-606.
112.顾晓东,余道衡,张立明.时延PCNN及其用于求解最短路径[J].电子学报,2004,Vol.32,No.9,pp：1441-1443.
113.Z.Yi, K.K.Tan. Dynamic stability conditions for Lotka Volterra recurrent neural networks with delays [J], Physical Review,2002, Vol.11, No.4, pp:320-327.
114. Johnson J. L. and Padgett M. L. PCNN models and applications [J]. IEEE Transactions on Neural Networks,1999, Vol.10, No.3, pp:480-498.
115.马义德,齐春亮.基于遗传算法的脉冲耦合神经网络自动系统的研究[J].系统仿真学报.2006,Vol.18,No.3,pp：722-725.
116. X.D. Gu, D.H. Yu, L.M. Zhang, Global icons and local icons of images based Unit-linking PCNN and their application to robot navigation[J]. Lecture Notes In Computer Science,3497,2005, pp: 836-841.
117.孔祥维,黄静,石浩.基于改进的脉冲耦合神经网络的红外目标分割方法[J].红外与毫米波学报,2001,Vol.20,No.5,pp：365-369.
118. X.D. Gu, D. H. Yu, L. M. Zhang. Image shadow removal using pulse coupled neural network[J]. IEEE Transaction Neural Networks, May 2005, Vol.16, No.3, pp:692-698.
119. Zhan F B. Three Fastest Shortest Path Algorithms on Real Road Networks Journal of Geograph [J]. Information and Decision Analysis,1997, Vol.1, No.1 pp:69-82.
120. Mortensen EN, Barrett WA. Intelligent scissors for image composition[C]. Computer Graphics SIGGRAPH'95 Proceedings. Los Angeles, CA, ACM SIGGRAPH,1995, pp:191-198.
121. Mortensen Eric N, Barrett William A. Interactive Segmentation with Intelligent Scissors [J]. Graphical Models and Image Processing.1998, Vol.60, No.5, pp:349-384.
122. Eckhorn R, Reitboeck H J, Arndt M, Dicke P. Feature liking via synchronization among distributed assemblies:Simulations of results from cat visual cortex [J]. Neural Computation. 1990, Vol.2, No.3, pp:293-307.
123.吴旭芒,高以群.足迹学[M].北京：警官教育出版社,1996.
124.李磊,童莉,平西建.平面赤足迹的形状分析[J].计算机辅助设计与图形学学报,2006,Vol.18,No.7,pp：976-981.
125.张涛,平西建,邵美珍.自回归模型在平面曲线识别中的应用[J].计算机研究与发展,Vol.37,No.8,2000,pp：942-947.
126.童莉,平西建,马金全,李磊.基于DFD参数变形模型的平面足迹轮廓提取[J].计算机辅助设计与图形学学报,2007,Vol·19,No·4,pp：521-527
127.黄群.赤脚足迹的统计分析[J].辽宁警专学报,2005,No.1,pp：5-9.
128.王卫东,平西建,丁益洪.立体足迹重压面提取与描述[J].微算计机信息,2005,Vol.21,No.11.pp：157-159.
129.王清举,平西建,王永栋.立体足迹计算机自动识别系统的应用[J].刑事技术,2003,No.5,pp：41-43.
130.雷航,童莉,平西建.平面赤足迹特征分析与身份识别方法[J].计算机辅助设计与图形学学报,2008,Vol.20,No.5,pp：659-664.
131.高以群,史力民.足迹检验图谱[M].北京：警官教育出版社,1997.
132.张强,王坤,郭丽,高立群.基于改进GVF和最小二乘法的弱边界椭圆提取[J].计算机应用,2007,Vol.27,No.4,pp：979-98.
133.黄贵玲,高西全,靳松杰,谈飞洋.基于蚁群算法的最短路径问题的研究和应用[J].计算机工程与应用,2007,Vol.43,No.13,pp：233-235.
134. Dorigo M., Gambardella LM. AntColonies for Traveling Salesman Problem[J]. BioSystems, 1997, Vol.43, pp:73-81.
135. Ying-Tung Hsiao, Cheng-Long Chnang, Cheng-Chih Chien. Ant Colony Optimization for Best Path Planning[C]. IEEE International Symposium on Communications and Information Technology, (ISCIT 2004), Oct.2004, Vol.1, pp:109-113.
136. Fitzgibbon AW, PiluM, Fischer R B. Direct least squares fitting of ellipses[C]. Proceedings of the 13th International Conference on Pattern Recognition Vienna,1996, pp:253-257.
137. Zhang, G, Jayas, D.S., and White, N.D.G. Separation of Touching Grain Kernels in an Image by Ellipse Fitting Algorithm[J]. Biosystems Engineering,2005, Vol.92, No.2, pp:135-142.
138.廖志军.交互式图像分割算法研究[D].北京：中国科学院计算机技术研究所,2006,6,pp：9-11.
139. A.Blake, C.Rother, M.Brown, P.Perez, P. Torr. Interactive Image Segmentation using an Adaptive GMMRF Model[C].In Proceeding European Conference on Computer Vision, (ECCV 2004, LNCS3021),pp:428-441.
140. Doyle P., Snell L.. Random walks and electric networks[M]. Number 22 in Carus mathematical monographs. Mathematical Association of America, Washington, D.C.,1984.
141. Fairfield, J., Toboggan contrast enhancement for contrast segmentation[C]. Proceedings of 10th International Conference on Pattern Recognition, June 1990, Vol.1,16-21, pp:712-716.
142. Yung-Chieh Lin, Yu-Pao Tsai, Yi-Ping Hung, Zen-Chung Shih. Comparison Between Immersion-Based and Toboggan-Based Watershed Image Segmentation[J]. IEEE Transactions on Image Processing, March 2006, Vol.15, No.3, pp:632-640.
143. Sinop, A. K., Grady, L. A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm. IEEE 11th International Conference on Computer Vision, (ICCV 2007),14-21 Oct.007, pp:1-8.
144. Genyun Sun, Qinhuo Liu, Qiang Liu, Changyuan Ji, Xiaowen Li. A novel approach for edge detection based on the theory of universal gravity[J]. Pattern Recognition, October 2007, Vol.40, No.10, pp:2766-2775.
145. Cheng H D,Sun Y.A Hierarchical Approach to Color Image Segmentation Using Homogeneity[J].IEEE Transaction on Image Processing,2000,Vol.9, No.12, pp:2071-2082.
146.张伟.基于视觉的运动车辆检测与跟踪[D].上海：上海交通大学,2007,pp：6.
147.赵建云,郑晓势,周伟,刘广起.基于视频的车辆检测与跟踪技术综述[J].计算机与信息技术,2007,No.4,pp：30-32.
148.胡铟,杨静宇.基于单目视觉的路面车辆检测及跟踪方法综述[J].公路交通科技.2007,Vol.24,No.12,pp：127-131.
149.朱明旱,罗大庸.基于帧间差分背景模型的运动物体检测与跟踪[J].指挥控制与仿真,2006,Vol.30 No.3,pp：1004-1009
150.刘宏,李锦涛,刘群.融合颜色和梯度特征的运动阴影消除方法[J].计算机辅助设计与图形学学报,Oct.2007,Vol.19,No.10,pp：1279-1285.
151. Malcolm, J., Rathi, Y., Tannenbaum, A. Multi-Object Tracking Through Clutter Using Graph Cuts[C]. IEEE 11th International Conference on Computer Vision. (ICCV 2007),14-21 Oct. 2007, pp:1-5.
152.侯叶,郭宝龙.基于图论的运动对象分割[J].吉林大学学报(工学版).2008,Vol.38,No.4,pp：902-906.
153.Wieclawek, W., Pietka, E. Live-Wire-Based 3D Segmentation MethodfC]. The 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, (EMBS 2007),22-26, Aug.2007, pp:5645-5648.
154. Sundaramoorthi, G, Yezzi, A., Mennucci, A. C. Coarse-to-Fine Segmentation and Tracking Using Sobolev Active Contours[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2008, Vol.30, No.5, pp:851-864.
155. Haritaoglu I, Harwood D, Davis L. W4:Real time surveillance of peop le and their activities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000, Vol.22, No.8, pp: 809-830.
156. Pfaltz J L, Rosenfeld A. Computer Representation of Planar Regions by Their Skeletons[J] Communications of ACM,1967, Vol.10, No.2, pp:119-122.
157.王华伟,李翠华,施华,韦凤梅.基于HSV空间和一阶梯度的阴影剪除算法[J],计算机工程与应用,2005,No.8,pp：43-45.
158.吴成东,郭利锋,张云洲,刘濛,多车辆跟踪时目标粘连的解决方法[J].东北大学学报(自然科学版)2008,Vol.129,No.8,pp：1065-1068.
159. Fairfield, John. Toboggan contrast enhancement[C]. Proceedings of SPIE-The International Society for Optical Engineering,1992, Vol.1708, pp:282-292.
160. Yao Xu, Hung, Yi-Ping. Fast image segmentation by sliding in the derivative terrain [C]. Proceedings of SPIE-The International Society for Optical Engineering,1992,Vol.1607, pp: 369-37.
161. Ibrahim, H., Petrou, M., Wells, K., Doran, S., Olsen, O. Preprocessing for use in automatic volumetric liver segmentation from NMR Data[C].2004 IEEE Nuclear Science Symposium Conference Record (IEEE Cat. No.04CH37604),2004, Vol.5, pp:2783-7.
162. Armstrong, C.J. Price, B.L., Barrett, W.A. Interactive segmentation of image volumes with Live Surface[J]. Computers & Graphics, April 2007, Vol.31, No.2, pp:212-29.
163. Mortensen E. N., Barrett W. A., Toboggan-based intelligent scissors with a four-parameter edge model[C]. IEEE Conferrence on Computer Vision and Pattern Recognition,1999, Vol.2, pp: 452-458.
164. Wang D.A. multiscale gradient algorithm for image segmentation using watershed[J]. Pattern Recognition,1997,301984, Vol.6, No.6, pp:661-674.
165. HarisK, Efstratiadis SerafimN, MaglaverasNicos. Hbrid image segmentation using watersheds and fast region mering[J]. IEEE Transactions on Image Processing,1998, Vol.2, pp:1684-1699.
166. Vincent L, Soille P. Watersheds in digital space:an efficient algorithm based on immersion simulations[J]. IEEE Transaction on Pattern. Analysis and Machine Intelligence.1991, Vol.13, No.6, pp:583-598.
167.卢官明.一种计算图像形态梯度的多尺度算法[J].中国图象图形学报,2001,Vol.6,No.3,pp：214-218.
168. Eckhorn R., Reitboeck H.J., Dicke M.A., P. Feature linking via synchronization among distributed assemblies:simulation of results from cat cortex[J]. Neural Computing.1990, Vol.2, pp:293-307.
169. Lindbald, T., Kinser, J.M.:Image processing using pulse-coupled neural networks[M]. Springer, 1998.
170. Yiyan Xue, Simon X. Yang. Image Segmentation Using Watershed Transform and Feed-Back Pulse Coupled Neural Network[J]. Lecture Notes in Computer Science, 2005, Vol.3696, pp:531-536.
171. Kinser, J.M., Johnson, J.L. Stabilized input with a feedback pulse-coupled neural network[J]. Optical Engineering.1996, Vol.35, pp:2158-2161.
172. Kuntimad, G., Ranganath, H. Perfect image segmentation using pulse coupled neural networks[J]. IEEE Transaction on Neural Networks,1999, Vol.10, pp:591-598.