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Abstrakty
Active contour model is a typical and effective closed edge detection algorithm, which has been widely applied in remote sensing image processing. Since the variety of the image data source, the complexity of the application background and the limitations of edge detection, the robustness and universality of active contour model are greatly reduced in the practical application of edge extraction. This study presented a fast edge detection approach based on global optimization convex model and Split Bregman algorithm. Firstly, the proposed approach defined a generalized convex function variational model which incorporated the RSF model’s principle and Chan’s global optimization idea and could get the global optimal solution. Secondly, a fast numerical minimization scheme based on split Bregman iterative algorithm is employed for overcoming drawbacks of noise and others. Finally, the curve evolves to the target boundaries quickly and accurately. The approach was applied in real special sea ice SAR images and synthetic images with noise, fuzzy boundaries and intensity inhomogeneity, and the experiment results showed that the proposed approach had a better performance than the edge detection methods based on the GMAC model and RSF model. The validity and robustness of the proposed approach were also verified.
Czasopismo
Rocznik
Tom
Strony
23--29
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
- School of Software, Dalian University of Foreign Languages, Dalian, Liaoning, China
autor
- School of Software, Dalian University of Foreign Languages, Dalian, Liaoning, China
autor
- School of Software, Dalian University of Foreign Languages, Dalian, Liaoning, China
autor
- Information Science and Technology College, Dalian Maritime University, Dalian, Liaoning, China
- School of Software, Dalian University of Foreign Languages, Dalian, Liaoning, China
Bibliografia
- 1. Bresson X, Chan T. Fast dual minimization of the vectorial total variation norm and applications to color image processing. Inverse Problems and Imaging 2008; 2(4): 455-484. http://dx.doi.org/10.3934/ipi.2008.2.455
- 2. Chan T, Vese L. Active contours without edges. IEEE Transactions on Image Processing 2001; 10(2): 266-277. http://dx.doi.org/10.1109/83.902291
- 3. Chan T. Algorithms for finding global minimizes of image segmentation and denoising models. SIAM J. Applied Mathematics 2006; 66(5): 1632-1648. http://dx.doi.org/10.1137/040615286
- 4. Clausi DA, Yue B. Comparing co-occurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery. IEEE Transaction on Geoscience and Remote Sensing 2004; 42(1) http://dx.doi.org/10.1109/TGRS.2003.817218
- 5. Cremers D, Rousson M, Deriche R. A review of statistical ap-proaches to level set segmentation: Integrating color, texture, motion and shape. Int. J. Comput. 2007; 72(2): 195–215. https://dx.doi.org/10.1007/s11263-006-8711-1
- 6. Goldstein T, Bresson X, Osher S. geometric applications of the split bregman method: segmentation and surface reconstruction. Journal of Scientific Computing 2010; 45(1-3): 272-293. http://dx.doi.org/10.1007/s10915-009-9331-z
- 7. Gui J, Rao X, Ying Y. Fruit shape detection by level set. Journal of Zhejiang University Science A 2007; 8(8):1232-1236 http://dx.doi.org/10.1631/jzus.2007.A1232
- 8. He L, Peng Z, Everding B, Wang X, Han C, Weiss K, Wee W. A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 2008; 26(5): 141–163. https://doi.org/10.1016/j.imavis.2007.07.010
- 9. Jing Y, An JB, Liu ZX. A novel edge detection algorithm based on global minimization active contour model for oil slick infrared aerial image. IEEE Transaction on Geoscience and Remote Sensing, 2011; 49(6): 2005-2013. http://dx.doi.org/10.1109/TGRS.2010.2103671
- 10. Jing Y, An JB, Wang YX, Liu Z X, Liu JX. A New Region-Based Active Contour Edge Detection Algorithm for Oil Spills Remote Sensing Image. Journal of Convergence Information Technology 2012; 7(21): 112-119.
- 11. Kwon TJ, Li J, Wong A. ETVOS: An Enhanced Total Variation Optimization Segmentation Approach for SAR Sea-Ice Image Segmentation. IEEE Transactions on geoscience and remote sensing 2013; 51(2):925-934. http://dx.doi.org/10.1109/TGRS.2012.2205259
- 12. Li C M, Kao C, Gore J. Minimization of regionscalable fitting energy for image segmentation. IEEE Transactions on Image Processing 2008; 17(10): 1940-1949. http://dx.doi.org/10.1109/TIP.2008.2002304
- 13. Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 1989; 42(5): 577-685. http://dx.doi.org/10.1002/cpa.3160420503
- 14. Osher S, Burger M, Goldfarb D, Xu J, Yin W. An iterative regularization method for total variationbased image restoration. Multiscale Model. Simul. 2005; 4: 460-489. http://dx.doi.org/10.1137/040605412
- 15. Torre V, Poggio T. On Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986; 8(2): 147-16. http://dx.doi.org/10.1109/TPAMI.1986.4767769
- 16. Wang L, Li C, Sun Q, Xia D, Kao C. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. J. Comput. Med. Imaging Graph. 2009; 33 (7): 520- 531. https://doi.org/10.1016/j.compmedimag.2009.04.010
- 17. Wu QG, An JB, Lin B. A Texture Segmentation Algorithm Based on PCA and Global Minimization Active Contour Model for Aerial Insulator Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2012; 5(5): 1509-1518. http://dx.doi.org/10.1109/JSTARS.2012.2197672
- 18. Yang YY, Zhao Y, Wu BY. Split Bregman Method for Minimization of Fast Multiphase Image Segmentation Model for Inhomogeneous Images. J Optim Theory Appl 2015; 166: 285-305. http://dx.doi.org/10.1007/s10957-014-0597-4
- 19. Yin W, Osher S, Goldfarb D, Darbon J. Bregman iterative algorithms for L1-minimization with applications to compressed sensing. SIAM J. Imaging Sci. 2008; 1(1): 143-168. http://dx.doi.org/10.1137/070703983
Typ dokumentu
Bibliografia
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