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Energy Matting

  • Yu Guan
  • Xiao Liang
  • Zi’ang Ding
  • Yinan Fan
  • Wei Chen
  • Qunsheng Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3942)

Abstract

In this paper, we formulate the image matting as one of solving energy minimization problems. Our approach has the following advantages. First, the matte estimation is modeled using an energy function as a MRF optimization problem. Second, the energy function combines the gradient of the matte, the gradient of the color and statistical sampling together to achieve global optimization. Third, the matte is directly reconstructed by solving energy equations. Experimental results show that our method is efficient to extract high quality mattes for foregrounds with complex natural images.

Keywords

Natural Image Markov Random Field Unknown Region Matte Estimation Belief Propagation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Berman, A., Vlahos, P., Dadourian, A.: Comprehensive method for removing from an image the background surrounding a selected object. U.S. Patent 6,134,345 (2000)Google Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  3. 3.
    Corel Corporation. Knockout user guide (2002)Google Scholar
  4. 4.
    Chuang, Y.-Y., Curless, B., Salesin, D.H., Szeliski, R.: A bayesian approach to digital matting. In: Proceedings of CVPR 2001, vol. II, pp. 264–271 (2001)Google Scholar
  5. 5.
    Rother, C., Blake, A., Kolmogorov, V.: Grabcut - interactive foreground extraction using iterated graph cuts. In: Proceedings of ACM SIGGRAPH 2004 (2004)Google Scholar
  6. 6.
    Ruzon, M.A., Tomasi, C.: Alpha estimation in natural images. In: Proceedings of CVPR 2000, pp. 18–25 (2000)Google Scholar
  7. 7.
    Sun, J., Jia, J., Tang, C.-K., Shum, H.-Y.: Poisson matting. In: Proceedings of ACM SIGGRAPH 2004, pp. 315–321 (2004)Google Scholar
  8. 8.
    Wang, J., Cohen, M.F.: An Iterative Optimization Approach for Unified Image Segmentation and Matting. In: ICCV 2005, pp. 936–943 (2005)Google Scholar
  9. 9.
    Weiss, Y., Freeman, W.T.: On the optimality of solutions of the max-product belief propagation algorithm in arebitrary graphs. IEEE Trans. on Information Theory 47(2), 303–308 (2001)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Wexler, Y., Fitzgibbon, A., Zisserman, A.: Bayesian estimation of layers from multiple images. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 487–501. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Weinman, J., Hanson, A., Mccallum, A.: Sign detection in natural images with conditional random fields. In: Proc. IEEE Workshop on Machine Learning for Signal Processing (September 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yu Guan
    • 1
  • Xiao Liang
    • 1
  • Zi’ang Ding
    • 1
  • Yinan Fan
    • 1
  • Wei Chen
    • 1
  • Qunsheng Peng
    • 1
  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina

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