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Alpha Matting of Motion-Blurred Objects in Bracket Sequence Images

  • Heesoo Myeong
  • Stephen Lin
  • Kyoung Mu Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

Abstract

We present a method that utilizes bracket sequence images to automatically extract the alpha matte of a motion-blurred object. This method makes use of a sharp, short-exposure snapshot in the sequence to help overcome major challenges in this task, including blurred object detection, spatially-variant object motion, and foreground/background color ambiguity. A key component of our matte estimation is the inference of approximate, spatially-varying motion of the blurred object with the help of the sharp snapshot, as this motion information provides important constraints on the aforementioned issues. In addition, we take advantage of other relationships that exist between a pair of consecutive short-exposure and long-exposure frames, such as common background areas and consistencies in foreground appearance. With this technique, we demonstrate successful alpha matting results on a variety of moving objects including non-rigid human motion.

Keywords

Alpha matting motion blur exposure bracketing 

References

  1. 1.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)CrossRefGoogle Scholar
  2. 2.
    Chakrabarti, A., Zickler, T., Freeman, W.T.: Analyzing spatially-varying blur. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2010)Google Scholar
  3. 3.
    Criminisi, A., Cross, G., Blake, A., Kolmogorov, V.: Bilayer segmentation of live video. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2006)Google Scholar
  4. 4.
    Dai, S., Wu, Y.: Motion from blur. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  5. 5.
    Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: ACM SIGGRAPH (1997)Google Scholar
  6. 6.
    Duchenne, O., Joulin, A., Ponce, J.: A graph-matching kernel for object categorization. In: Proc. Int’l Conf. on Computer Vision, ICCV (2011)Google Scholar
  7. 7.
    HaCohen, Y., Shechtman, E., Goldman, D.B., Lischinski, D.: Nrdc: Non-rigid dense correspondence with applications for image enhancement. In: ACM Trans. Graph (2011)Google Scholar
  8. 8.
    HaCohen, Y., Shechtman, E., Lischinski, D.: Deblurring by example using dense correspondence. In: Proc. Int’l Conf. on Computer Vision, ICCV (2013)Google Scholar
  9. 9.
    Jia, J.: Single image motion deblurring using transparency. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2007)Google Scholar
  10. 10.
    Köhler, R., Hirsch, M., Schölkopf, B., Harmeling, S.: Improving alpha matting and motion blurred foreground estimation. In: Proc. Int’l Conf. on Image Processing, ICIP (2013)Google Scholar
  11. 11.
    Levin, A.: Blind motion deblurring using image statistics. In: NIPS (2006)Google Scholar
  12. 12.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)CrossRefGoogle Scholar
  13. 13.
    Lin, H.T., Tai, Y.W., Brown, M.S.: Motion regularization for matting motion blurred objects. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2329–2336 (2011)CrossRefGoogle Scholar
  14. 14.
    Nayar, S.K., Ben-Ezra, M.: Motion-based motion deblurring. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 689–698 (2004)CrossRefGoogle Scholar
  15. 15.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)CrossRefGoogle Scholar
  16. 16.
    Shan, Q., Xiong, W., Jia, J.: Rotational motion deblurring of a rigid object from a single image. In: Proc. Int’l Conf. on Computer Vision, ICCV (2007)Google Scholar
  17. 17.
    Sheikh, Y., Javed, O., Kanade, T.: Background subtraction for freely moving cameras. In: Proc. Int’l Conf. on Computer Vision, ICCV (2009)Google Scholar
  18. 18.
    Sun, J., Lin, Y., Kang, S.B., Shum, H.Y.: Flash matting. ACM Trans. Graph. 25(3), 772–778 (2006)CrossRefGoogle Scholar
  19. 19.
    Sun, J., Sun, J., Kang, S.B., Xu, Z.B., Tang, X., Shum, H.Y.: Flash cut: Foreground extraction with flash and no-flash image pairs. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2007)Google Scholar
  20. 20.
    Tai, Y.W., Du, H., Brown, M.S., Lin, S.: Image/video deblurring using a hybrid camera. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  21. 21.
    Tai, Y.W., Du, H., Brown, M.S., Lin, S.: Correction of spatially varying image and video motion blur using a hybrid camera. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1012–1028 (2010)CrossRefGoogle Scholar
  22. 22.
    Tai, Y.W., Kong, N., Lin, S., Shin, S.Y.: Coded exposure imaging for projective motion deblurring. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2010)Google Scholar
  23. 23.
    Wang, J., Cohen, M.F.: Optimized color sampling for robust matting. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2007)Google Scholar
  24. 24.
    Wang, J., Cohen, M.F.: Image and Video Matting. NOW Publishers Inc. (2008)Google Scholar
  25. 25.
    Wang, L., Xia, T., Guo, Y., Liu, L., Wang, J.: Confidence-driven image co-matting. Computers & Graphics 38(2), 131–139 (2013)Google Scholar
  26. 26.
    Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26(3) (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Heesoo Myeong
    • 1
  • Stephen Lin
    • 2
  • Kyoung Mu Lee
    • 1
  1. 1.Department of ECE, ASRISeoul National UniversityKorea
  2. 2.Microsoft ResearchChina

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