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A Mobile-Oriented Hand Segmentation Algorithm Based on Fuzzy Multiscale Aggregation

  • Ángel García-Casarrubios Muñoz
  • Carmen Sánchez Ávila
  • Alberto de Santos Sierra
  • Javier Guerra Casanova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)

Abstract

We present a fuzzy multiscale segmentation algorithm aimed at hand images acquired by a mobile device, for biometric purposes. This algorithm is quasi-linear with the size of the image and introduces a stopping criterion that takes into account the texture of the regions and controls the level of coarsening. The algorithm yields promising results in terms of accuracy segmentation, having been compared to other well-known methods. Furthermore, its procedure is suitable for a posterior mobile implementation.

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References

  1. 1.
    Li, Y., Xu, X.: Revolutionary Information System Application in Biometrics. In: International Conference on Networking and Digital Society, ICNDS 2009, May 30-31, vol. 1, pp. 297–300 (2009)Google Scholar
  2. 2.
    Fong, L.L., Seng, W.C.: A Comparison Study on Hand Recognition Approaches. In: International Conference of Soft Computing and Pattern Recognition, SOCPAR 2009, December 4-7, pp. 364–368 (2009)Google Scholar
  3. 3.
    Shirakawa, S., Nagao, T.: Evolutionary image segmentation based on multiobjective clustering. In: IEEE Congress on Evolutionary Computation, CEC 2009, May 18-21, pp. 2466–2473 (2009)Google Scholar
  4. 4.
    Kang, W.-X., Yang, Q.-Q., Liang, R.-P.: The Comparative Research on Image Segmentation Algorithms. In: First International Workshop on Education Technology and Computer Science, ETCS 2009, March 7-8, vol. 2, pp. 703–707 (2009)Google Scholar
  5. 5.
    Sharon, E., Galun, M., Sharon, D., Basri, R., Brandt, A.: Hierarchy and adaptivity in segmenting visual scenes. Macmillan Publishing Ltd., Basingstoke (2006)Google Scholar
  6. 6.
    Son, T.T., Mita, S., Takeuchi, A.: Road detection using segmentation by weighted aggregation based on visual information and a posteriori probability of road regions. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, October 12-15, pp. 3018–3025 (2008)Google Scholar
  7. 7.
    Sharon, E., Brandt, A., Basri, R.: Fast multiscale image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, Proceedings, vol. 1, pp. 70–77 (2000)Google Scholar
  8. 8.
    Sharon, E., Brandt, A., Basri, R.: Segmentation and boundary detection using multiscale intensity measurements. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-469 – I-476 (2001)Google Scholar
  9. 9.
    Rory Tait Neilson, B.N., McDonald, S.: Image segmentation by weighted aggregation with gradient orientation histograms. In: Southern African Telecommunication Networks and Applications Conference, SATNAC (2007)Google Scholar
  10. 10.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Computer Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  11. 11.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (June 2007)Google Scholar
  12. 12.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  13. 13.
    Comaniciu, D., Meer, P., Member, S.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  14. 14.
    Dyer, R., Zhang, H., Möller, T.: Delaunay mesh construction. In: Proceedings of the Fifth Eurographics Symposium on Geometry Processing, SGP 2007, Aire-la-Ville, Switzerland, pp. 273–282. Eurographics Association (2007)Google Scholar
  15. 15.
    Vassili, V.V., Sazonov, V., Andreeva, A.: A survey on pixel-based skin color detection techniques. In: Proc. Graphicon 2003, pp. 85–92 (2003)Google Scholar
  16. 16.
    Hunter, R.S.: Photoelectric Color-Difference Meter. Proceedings of the Winter Meeting of the Optical Society of America, JOSA 38(7), 661 (1948)Google Scholar
  17. 17.
    de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications, 3rd edn., Springer, Heidelberg (April 2008)Google Scholar
  18. 18.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston (1992)Google Scholar
  19. 19.
    Meirav, G., Eitan, S., Basri, R., Brandt, A.: Texture segmentation by multiscale aggregation of filter responses and shape elements. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, ICCV 2003, Washington, DC, USA, p. 716. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  20. 20.
    Xiao, Q., Zhang, N., Gao, S., Li, F., Gao, Y.: Segmentation based on shape prior and graph model optimization. In: 2nd International Conference on Advanced Computer Control (ICACC), March 27-29, vol. 3, pp. 405–408 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ángel García-Casarrubios Muñoz
    • 1
  • Carmen Sánchez Ávila
    • 1
  • Alberto de Santos Sierra
    • 1
  • Javier Guerra Casanova
    • 1
  1. 1.Group of Biometrics, Biosignals and Security, GB2S, Centro de Domótica IntegralUniversidad Politécnica de MadridSpain

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