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Motion Object Segmentation Using Regions Classification and Energy Model

  • Xiaokun Zhang
  • Xuying Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 107)

Abstract

An automatic video object segmentation is proposed. The video scene is partitioned into some homogeneous regions by an automatically cluster to form regions method. Then these regions are initially classified into three categories: moving object, candidate and background using the difference information between two successive frames. The spatio-temporal energy model is constructed to determine the candidate regions to moving object or background. Some post-processing methods are utilized to achieve the more accurate segmentation object. Experimental results show that the spatial accuracy of our proposed algorithm improves about 30–50% and temporal coherency improves about 0.05–0.70 than COST211 AM.

Keywords

Video processing Motion segmentation Regions selection 

References

  1. 1.
    Zhang DS, Lu GJ (2001) Segmentation of moving object in image sequence: a review. Circuits Syst Signal Process 20(2):143–183CrossRefMATHGoogle Scholar
  2. 2.
    Gu C, Lee M-C (1998) Semiautomatic segmentation and tracking of semantic video objects. IEEE Trans Circuits Syst Video Technol 8(5):572–584CrossRefGoogle Scholar
  3. 3.
    Tsaig Y, Averbuch A (2002) Automatic segmentation of moving objects in video sequences: a region labeling approach. IEEE Trans. Circuits Syst Video Technol 7(12):597–612CrossRefGoogle Scholar
  4. 4.
    Gelon M, Bouthemy P (2000) A region-level motion-based graph representation and labeling for tracking a spatial image partition. Pattern Recognit 33:725–740CrossRefGoogle Scholar
  5. 5.
    Patras I, Hendricks EA, Lagendijk RL (2001) Video segmentation by MAP Labeling of watershed segments. IEEE Trans Pattern Anal Mach Intell 23(3):326–331Google Scholar
  6. 6.
    Vincent L, Serra J (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–589CrossRefGoogle Scholar
  7. 7.
    Wang D (1998) Unsupervised video segmentation based on watersheds and temporal tracking. IEEE Trans Circuits Syst Video Technol 8(5):539–546CrossRefGoogle Scholar
  8. 8.
    Mech R, Wollborn M (1998) A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera. Signal Process 66:203–217CrossRefMATHGoogle Scholar
  9. 9.
    Wollborn M, Mech R (1998) Refined procedure for objective evaluation of video object segmentation algorithms [R]. Doc. ISO/IEC JTC1/SC29/WG11 M3448, March 1998Google Scholar
  10. 10.
    COST211 AM. Working site for sequences and algorithms exchange. http://www.tele.ucl.ac.be/exchange

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Beijing Electronic Science and Technology InstituteBeijingPeople’s Republic of China

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