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

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Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6939))

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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.

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References

  1. Boykov, Y., Funk-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. Journal of Computer Vision 70, 109–131 (2006)

    Article  Google Scholar 

  2. Malcolm, J., Rathi, Y., Tannenbaum, A.: Tracking through clutter using graph cuts. In: British Machine Vision Conf., BMVC (2007)

    Google Scholar 

  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. 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. Southall, B., Taylor, C.: Stochastic road shape estimation. In: Proc. Int. Conf. Computer Vision, pp. 205–212 (2001)

    Google Scholar 

  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. 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. Rasmussen, C., Lu, Y., Kocamaz, M.: Trail following with omnidirectional vision. In: Proc. Int. Conf. Intelligent Robots and Systems (2010)

    Google Scholar 

  9. Rother, C., Kolmogorov, V., Blake, A.: Grabcut - interactive foreground extraction using iterated graph cuts. In: SIGGRAPH (2004)

    Google Scholar 

  10. Kocamaz, M., Rasmussen, C.: Automatic refinement of foreground regions for robot trail following. In: Proc. Int. Conf. Pattern Recognition (2010)

    Google Scholar 

  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. Mehrani, P., Veksler, O.: Saliency segmentation based on learning and graph cut refinement. In: Proc. British Machine Vision Conference (2010)

    Google Scholar 

  13. Nelson, A., Neubert, J.: Object tracking via graph cuts. In: SPIE Applications of Digital Image Processing (2009)

    Google Scholar 

  14. Papadakis, N., Bugeau, A.: Tracking with occlusions via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence 33, 144–157 (2011)

    Article  Google Scholar 

  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. 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. 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

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, University of Delaware, Newark, DE, U.S.A.

    Mehmet Kemal Kocamaz, Yan Lu & Christopher Rasmussen

Authors
  1. Mehmet Kemal Kocamaz
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  2. Yan Lu
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  3. Christopher Rasmussen
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, West Lafayette, 47907-2021, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana at Lafayette, 70504, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Kocamaz, M.K., Lu, Y., Rasmussen, C. (2011). Deformable Object Shape Refinement and Tracking Using Graph Cuts and Support Vector Machines. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_51

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  • DOI: https://doi.org/10.1007/978-3-642-24031-7_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24030-0

  • Online ISBN: 978-3-642-24031-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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