The Visual Computer

, Volume 29, Issue 1, pp 27–39 | Cite as

Fast level set image and video segmentation using new evolution indicator operators

  • Chunxia XiaoEmail author
  • Jiajia Gan
  • Xiangyun Hu
Original Article


We propose an effective level set evolution method for robust object segmentation in real images. We construct an effective region indicator and an multiscale edge indicator, and use these two indicators to adaptively guide the evolution of the level set function. The multiscale edge indicator is defined in the gradient domain of the multiscale feature-preserving filtered image. The region indicator is built on the similarity map between image pixels and user specified interest regions, where the similarity map is computed using Gaussian Mixture Models (GMM). Then we combine these two methods to develop a new mixing edge stop function, which makes the level set method more robust to initial active contour setting, and forces the level set to evolve adaptively based on the image content. Furthermore, we apply an acceleration approach to speed up our evolution process, which yields real time segmentation performance. Finally, we extend the proposed approach to video segmentation for achieving effective target tracking results. As the results show, our approach is effective for image and video segmentation and works well to accurately detect the complex object boundaries in real-time.


Level set Segmentation Gaussian mixture models Filtering Tracking 



This work was partly supported by the National Basic Research Program of China (No. 2012CB725303), NSFC (No. 60803081, No. 61070081), Open Project Program of the State Key Laboratory for Novel Software Technology (kfkt2010B05), the Open Project Program of the State Key Lab of CAD&CG (Grant No. A1208), and Luojia Outstanding Young Scholar Program of Wuhan University.

Supplementary material

(AVI 371 kB)

(AVI 371 kB)

(AVI 252 kB)

(AVI 327 kB)


  1. 1.
    Adalsteinsson, D., Sethian, J.: A fast level set method for propagating interfaces. J. Comput. Phys. 118(2), 269–277 (1995) MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Bai, X., Sapiro, G.: A geodesic framework for fast interactive image and video segmentation and matting (2007) Google Scholar
  3. 3.
    Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: ICCV 2001, vol. 1, pp. 105–112. IEEE, New York (2001) Google Scholar
  4. 4.
    Caselles, V., Catté, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66(1), 1–31 (1993) MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997) zbMATHCrossRefGoogle Scholar
  6. 6.
    Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001) zbMATHCrossRefGoogle Scholar
  7. 7.
    Chen, J., Paris, S., Durand, F.: Real-time edge-aware image processing with the bilateral grid. ACM Trans. Graph. (TOG) 26(3), 103 (2007) CrossRefGoogle Scholar
  8. 8.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vis. 72(2), 195–215 (2007) CrossRefGoogle Scholar
  9. 9.
    Criminisi, A., Sharp, T., Blake, A.: Geos: Geodesic image segmentation. In: ECCV’08 Proceedings of the 10th European Conference on Computer Vision: Part I. Springer, Marseille (2008) Google Scholar
  10. 10.
    Duchenne, O., Audibert, J., Keriven, R., Ponce, J., Ségonne, F.: Segmentation by transduction. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE, New York (2008) Google Scholar
  11. 11.
    Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 67 (2008) CrossRefGoogle Scholar
  12. 12.
    Fattal, R., Agrawala, M., Rusinkiewicz, S.: Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. 26(3), 51 (2007) CrossRefGoogle Scholar
  13. 13.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988) CrossRefGoogle Scholar
  14. 14.
    Lefohn, A., Kniss, J., Hansen, C., Whitaker, R.: Interactive deformation and visualization of level set surfaces using graphics hardware. In: IEEE Visualization 2003, p. 11. IEEE Comput. Soc., Los Alamitos (2003) Google Scholar
  15. 15.
    Li, C., Xu, C., Gui, C., Fox, M.: Level set evolution without re-initialization: A new variational formulation. In: CVPR, pp. 1063–6919 (2005) Google Scholar
  16. 16.
    Li, Y., Sun, J., Tang, C., Shum, H.: Lazy snapping. ACM Trans. Graph. (TOG) 23(3), 303–308 (2004) CrossRefGoogle Scholar
  17. 17.
    Liao, B., Xiao, C., Liu, M., Dong, Z., Peng, Q.: Fast hierarchical animated object decomposition using approximately invariant signature. The Visual Computer, pp. 1–13 (2011) Google Scholar
  18. 18.
    Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Springer, Berlin (2003) zbMATHGoogle Scholar
  19. 19.
    Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988) MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Paragios, N., Deriche, R.: Geodesic active regions for supervised texture segmentation. In: ICCV, p. 688. IEEE Comput. Soc., Los Alamitos (1999) Google Scholar
  21. 21.
    Peng, D., Merriman, B., Osher, S., Zhao, H., Kang, M.: A PDE-based fast local level set method* 1. J. Comput. Phys. 155(2), 410–438 (1999) MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Pock, T., Chambolle, A., Cremers, D., Bischof, H.: A convex relaxation approach for computing minimal partitions (2009) Google Scholar
  23. 23.
    Qu, Y., Wong, T., Heng, P.: Manga colorization. ACM Trans. Graph. (TOG) 25(3), 1214–1220 (2006) CrossRefGoogle Scholar
  24. 24.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM Transactions on Graphics (TOG), vol. 23, pp. 309–314. ACM, New York (2004) Google Scholar
  25. 25.
    Santner, J., Pock, T., Bischof, H.: Interactive multi-label segmentation. In: ACCV, pp. 397–410 (2011) Google Scholar
  26. 26.
    Sethian, J.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (2000) Google Scholar
  27. 27.
    Shi, Y., Karl, W.: Real-Time Tracking Using Level Sets. In: CVPR, pp. 34–41 (2005) Google Scholar
  28. 28.
    Sussman, M., Fatemi, E.: An efficient, interface-preserving level set redistancing algorithm and its application to interfacial incompressible fluid flow. SIAM J. Sci. Comput. 20(4), 1165–1191 (1999) MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846. IEEE, New York (1998) Google Scholar
  30. 30.
    Unger, M., Pock, T., Trobin, W., Cremers, D., Bischof, H.: Tvseg-interactive total variation based image segmentation. In: BMVC, Leeds, UK (2008) Google Scholar
  31. 31.
    Vemuri, B., Chen, Y.: Joint image registration and segmentation. Geometric level set methods in imaging, vision, and graphics, pp. 251–269 (2003) Google Scholar
  32. 32.
    Xia, T., Wu, Q., Chen, C., Yu, Y.: Lazy texture selection based on active learning. Vis. Comput. 26(3), 157–169 (2010) CrossRefGoogle Scholar
  33. 33.
    Xiao, C., Gan, J., Hu, X.: Fast level set image segmentation using new evolution indicator operators. In: Pacific Graphics (2011), short paper. Wiley Online Library Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina
  2. 2.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina

Personalised recommendations