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Motion Field Refinement and Region-Based Motion Segmentation

  • Sun-Kyoo Hwang
  • Whoi-Yul Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3767)

Abstract

In this paper, we propose a method to refine a motion field from image sequences and region-based motion segmentation using the motion information. An initial motion field is generated by a block matching algorithm. We compute the motion profile at each block and define the motion confidence measure from the motion profile. In the refining process, we regulate the motion vectors with low confidence to those with high confidence. In the segmentation stage, each frame of the image sequence is partitioned into regions by a watershed algorithm and a motion vector is assigned to each region. After constructing a region adjacency graph, the graph is segmented by the normalized cuts algorithm. The experiments show that the proposed method provides satisfactory results in motion segmentation from image sequences with or without camera motion.

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References

  1. 1.
    MPEG-4 Video Verification Model Version 15.0, ISO/IEC JTC1/SC29/WG11 N3093 (1999)Google Scholar
  2. 2.
    Bober, M.: MPEG-7 Visual Shape Descriptors. IEEE Trans. Circuits and Systems for Video Technology 11(6), 716–719 (2001)CrossRefGoogle Scholar
  3. 3.
    Tsaig, Y., Averbuch, A.: Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach. IEEE Trans. Circuits and Systems for Video Technology 12(7), 597–612 (2002)CrossRefGoogle Scholar
  4. 4.
    Shi, J., Malik, J.: Motion segmentation and tracking using normalized cuts. In: Sixth International Conference on Computer Vision, pp. 1154–1160 (1998)Google Scholar
  5. 5.
    Smith, P., Drummond, T., Cipolla, R.: Layered motion segmentation and depth ordering by tracking edges. IEEE Trans. Pattern Analysis and Machine Intelligence 26(4), 479–494 (2004)CrossRefGoogle Scholar
  6. 6.
    Tekalp, A.M.: Digital video processing, pp. 72–116. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  7. 7.
    Zhu, S., Ma, K.-K.: A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation. IEEE Trans. Image Processing 9(2), 287–290 (2000)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Trans. Image Processing 7(3), 359–369 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  10. 10.
    De Smet, P., De Vleeschauwer, D.: Performance and Scalability of a highly optimized rainfalling watershed algorithm. In: Proc. Int. Conf. on Image Science, Systems and technology, pp. 266–273 (1998)Google Scholar
  11. 11.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  12. 12.
    Strang, G.: Introduction to Applied Mathematics. Wellesley-Cambridge Press (1986)Google Scholar
  13. 13.
    Perona, P., Malik, J.: Scale Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  14. 14.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  15. 15.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Analysis and Machine Intelligence 6, 721–741 (1984)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sun-Kyoo Hwang
    • 1
  • Whoi-Yul Kim
    • 1
  1. 1.Division of Electrical and Computer EngineeringHanyang UniversitySeoulKorea

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