Deformable Object Shape Refinement and Tracking Using Graph Cuts and Support Vector Machines

  • Mehmet Kemal Kocamaz
  • Yan Lu
  • Christopher Rasmussen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

This paper describes several approaches to the problem of obtaining a refined segmentation of an object given a coarse initial segmentation of it. One line of investigation modifies the standard graph cut method by incorporating color and shape distance terms, adaptively weighted at run time to try to favor the most informative cue given visual conditions. We also discuss a machine learning approach based on support vector machines which uses color and spatial features as well. Furthermore, we extend these single-frame refinement methods to serve as the basis of trackers which work for a variety of object types with complex, deformable shapes. Comparative results are presented for several diverse datasets including objects such as trail regions used for robot navigation, hands, and faces.

Keywords

Support Vector Machine Color Space Training Image Object Segmentation Object Region 
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.
    Boykov, Y., Funk-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. Journal of Computer Vision 70, 109–131 (2006)CrossRefGoogle Scholar
  2. 2.
    Malcolm, J., Rathi, Y., Tannenbaum, A.: Tracking through clutter using graph cuts. In: British Machine Vision Conf., BMVC (2007)Google Scholar
  3. 3.
    Burges, C.: A tutorial on support vector machines for pattern recognition. In: Data Mining and Knowledge Discovery, pp. 121–167 (1998)Google Scholar
  4. 4.
    Taylor, C., Malik, J., Weber, J.: A real-time approach to stereopsis and lane- finding. In: Proc. IEEE Intelligent Vehicles Symposium (1996)Google Scholar
  5. 5.
    Southall, B., Taylor, C.: Stochastic road shape estimation. In: Proc. Int. Conf. Computer Vision, pp. 205–212 (2001)Google Scholar
  6. 6.
    Huang, A., Moore, D., Antone, M., Olson, E., Teller, S.: Multi-sensor lane finding in urban road networks. In: Robotics: Science and Systems (2008)Google Scholar
  7. 7.
    Rasmussen, C., Lu, Y., Kocamaz, M.: Appearance contrast for fast, robust trail- following. In: Proc. Int. Conf. Intelligent Robots and Systems (2009)Google Scholar
  8. 8.
    Rasmussen, C., Lu, Y., Kocamaz, M.: Trail following with omnidirectional vision. In: Proc. Int. Conf. Intelligent Robots and Systems (2010)Google Scholar
  9. 9.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut - interactive foreground extraction using iterated graph cuts. In: SIGGRAPH (2004)Google Scholar
  10. 10.
    Kocamaz, M., Rasmussen, C.: Automatic refinement of foreground regions for robot trail following. In: Proc. Int. Conf. Pattern Recognition (2010)Google Scholar
  11. 11.
    Dinh, T., Medioni, G.: Two-frames accurate motion segmentation using tensor voting and graph-cuts. In: IEEE Workshop on Motion and Video Computing (2008)Google Scholar
  12. 12.
    Mehrani, P., Veksler, O.: Saliency segmentation based on learning and graph cut refinement. In: Proc. British Machine Vision Conference (2010)Google Scholar
  13. 13.
    Nelson, A., Neubert, J.: Object tracking via graph cuts. In: SPIE Applications of Digital Image Processing (2009)Google Scholar
  14. 14.
    Papadakis, N., Bugeau, A.: Tracking with occlusions via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence 33, 144–157 (2011)CrossRefGoogle Scholar
  15. 15.
    Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proc. Int. Conf. Computer Vision (2001)Google Scholar
  16. 16.
    Sclaroff, S., Liu, L.: Deformable shape detection and description via model-based region grouping. IEEE Trans. Pattern Analysis and Machine Intelligence 23 (2001)Google Scholar
  17. 17.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mehmet Kemal Kocamaz
    • 1
  • Yan Lu
    • 1
  • Christopher Rasmussen
    • 1
  1. 1.Department of Computer and Information SciencesUniversity of DelawareNewarkU.S.A.

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