|
|
|
| An Adaptive C-V Image Segmentation Model Guided by Gray Difference Energy Function |
| WANG Xiang-Hai1,2, WANG Jin-Ling1 , FANG Ling-Ling1,2 |
1. School of Computer and Information Technology, Liaoning Normal University, Dalian 116029
2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006 |
|
|
|
|
Abstract As the sign of geometric active contour model (GACM), the C-V model has robustness to obscured targets and edge noise in image segmentation. However, this model usually cannot deal with complex heterogeneous images, and it is also sensitive to the initial position of evolution curve and has a high computational complexity. The more the average gray difference between inner region and outer region is, the closer the evolutionary curve to accurate target edge is. On this basis, an adaptive C-V image segmentation model guided by gray difference energy function is proposed. The model can adjust the movement trend of the evolutionary curve by the guidance function constructed based on average gray difference between inner region and outer region adaptively. This makes the evolution of the curve within a valid narrow band scope. The proposed model ensures the local homogeneity of gray calculation of contour curve between inner region and outer region and enhances the ability to capture the detailed target. At the same time, it improves the calculation speed of the model and the adaptability to the initial position of evolution curve to a certain extent. A large number of simulation experiments verify the validity of the proposed model.
|
|
Received: 08 October 2013
|
|
|
|
|
|
[1] Wang D K, Hou Y Q, Peng J Y. Partial Differential Equations Method in Image Processing. Beijing, China: Science Press, 2008(in Chinese)
(王大凯,侯榆青,彭进业.图像处理的偏微分方程方法.北京:科学出版社, 2008)
[2] Kass M, Witkin A, Terzopoulos D. Snakes: Active Contour Models. International Journal of Computer Vision, 1988, 1(4): 321-331
[3] Chen B, Lai J H. Active Contour Models on Image Segmentation: A Survey. Journal of Image and Graphics, 2007, 12(1): 11-20 (in Chinese)
(陈 波,赖剑煌.用于图像分割的活动轮廓模型综述.中国图象图形学报, 2007, 12(1): 11-20)
[4] Wang X H, Fang L L. Survey of Image Segmentation Based on Active Contour Model. Pattern Recognition and Artificial Intelligence, 2013, 26(8): 751-760 (in Chinese)
(王相海,方玲玲.活动轮廓模型的图像分割方法综述.模式识别与人工智能, 2013, 26(8): 751-760)
[5] Caselles V, Kimmel R, Saprio G. Geodesic Active Contours. International Journal of Computer Vision, 1997, 22(1): 61-79
[6] Paragios N, Deriche R. Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. International Journal of Computer Vision, 2002, 46(3): 223-247
[7] Kim J, Fisher J W, Yezzi A, et al. A Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution. IEEE Trans on Image Processing, 2005, 14(10): 1486-1502
[8] Herbulot A, Jehan-Besson S, Barlaud M, et al. Shape Gradient for Image Segmentation Using Information Theory // Proc of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Montreal, Canada, 2004, III: 21-24
[9] Aubert G, Barlaud M, Faugeras O, et al. Image Segmentation Using Active Contours: Calculus of Variations or Shape Gradients? SIAM Journal on Applied Mathematics, 2005, 63(6): 2128-2154
[10] Tsai A, Yezzi A, Willsky A S. Curve Evolution Implementation of the Mumford-Shah Functional for Image Segmentation, Denoising, Interpolation, and Magnification. IEEE Trans on Image Processing, 2001, 10(8): 1169-1186
[11] Chan T T, Vese L A. Active Contours without Edges. IEEE Trans on Image Processing, 2001, 10(2): 266-277
[12] Mumford D, Shah J. Optimal Approximations of Piecewise Smooth Functions and Associated Variational Problems. Communications on Pure and Applied Mathematics, 1989, 42(5): 577-685
[13] Shi J B, Malik J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905
[14] Zhang B, Su Y L, Xu Y F, et al. An Adaptive Geodesic Active Contour Model // Proc of the 6th International Conference on Natural Computation. Yantai, China, 2010, 5: 2267-2270
[15] Paragios N, Deriche R. Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(3): 266-280
[16] Zhang J W, Ge Q. MR Image Segmentation of Fast CV Model Based on Local Statistic Information. Journal of Image and Graphics, 2010, 15(1): 69-74(in Chinese)
(张建伟,葛 琦.基于局部统计信息的快速CV模型MR图像分割.中国图象图形学报, 2010, 15(1): 69-74)
[17] Zhu X S, Sun Q S, Xia D S. Adaptive CV Model Using Convex Optimization. Application Research Computers, 2012, 29(2): 779-781 (in Chinese)
(朱晓舒,孙权森,夏德深.基于凸优化的自适应CV模型.计算机应用研究, 2012, 29(2): 779-781)
[18] Li C M, Kao C Y, Gore J C, et al. Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Trans on Image Processing, 2008, 17(10): 1940-1949
[19] Osher S, Sethian J A. Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. Journal of Computational Physics, 1988, 79(1): 12-49
[20] Xu L L, Xiao J S, Yi B S, et al. An Improved C-V Image Segmentation Method Based on Level Set Model // Proc of the 1st International Conference on Intelligent Networks and Intelligent Systems. Wuhan, China, 2008: 507-510
[21] Wang Z, Wang Y P, Li S W. Tire Impressions Image Segmentation Algorithm Based on C-V Model without Re-initialization // Proc of the 3rd IEEE International Conference on Communication Software and Networks. Xi′an, China, 2011: 541 - 545
[22] Xu D, Peng Z M, Yong Y. An Improved Image Segmentation Method Based on Fast Level Set Combining with C-V Model // Proc of the Spring Congress on Engineering and Technology. Xi′an, China, 2012: 1-4
[23] Wang X H, Li M. Level Set Model for Image Segmentation Based on Dual Contour Evolutional Curve. Journal of Image and Graphics, 2014, 19(3): 373-380 (in Chinese)
(王相海,李 明.双重轮廓演化曲线的图像分割水平集模型.中国图象图形学报, 2014, 19(3): 373-380)
[24] Zhu G P. Image Segmentation Based on Active Contour Model. Ph.D Dissertation. Harbin, China: Harbin Institute of Technology, 2007(in Chinese)
(朱国普.基于活动轮廓模型的图像分割.博士学位论文. 哈尔滨:哈尔滨工业大学. 2007) |
|
|
|
2015, Vol. 28 
