New Eye Contact Correction Using Radial Basis Function for Wide Baseline Videoconference System

  • Xiaozhou Zhou
  • Pierre Boulanger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7674)

Abstract

In this paper, we introduce a novel eye contact correction method for videoconference systems with wide baseline. In this system, assistant cameras are installed on each side of the monitor to help capture the views from left side and right side. A pattern with random dots and Radial Basis Function (RBF) interpolation are used to help create precise disparity maps that is then used for re-projection. The interpolated views show a smooth transit among cameras on two sides. The experimental results also demonstrate that the proposed method could be extended to produce more robust and accurate disparity maps than most of the existing algorithms from regular stereo images.

Keywords

eye contact correction immersive videoconference computer vision stereo radial basis function interpolation 

References

  1. 1.
    Ott, M., Lewis, J., Cox, I.: Teleconferencing Eye Contact Using a Virtual Camera. In: INTERCHI, pp. 119–110 (1993)Google Scholar
  2. 2.
    Lei, B.J., Hendriks, E.A.: Real-time Multi-step View Reconstruction for a Virtual Teleconference System. Journal on Applied Signal Processing 2002, 1067–1087 (2002)MATHCrossRefGoogle Scholar
  3. 3.
    Schreer, O., Hendriks, E., Schraagen, J., Stone, J., Trucco, E., Jewell, M.: Virtual Team User Environment – A Key Application in Telecommunication. In: Proceeding of eBusiness and eWork, Prague, pp. 916–923 (2002)Google Scholar
  4. 4.
    Baker, H.H., Tanguay, D., Sobel, I., Gelb, D., Goss, M.E., Culbertson, W.B., Malzbender, T.: The Coliseum Immersive Teleconferencing System. In: Proceedings of International Workshop on Immersive Telepresence (2002)Google Scholar
  5. 5.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1), 7–42 (2002)MATHCrossRefGoogle Scholar
  6. 6.
  7. 7.
    Tao, H., Sawhney, H.S., Kumar, R.: A Global Matching Framework for Stereo Computation. In: Proceedings of ICCV, vol. 1, pp. 532–539 (2001)Google Scholar
  8. 8.
    Hong, L., Chen, G.: Segment-based Stereo Matching Using Graph Cuts. In: Proceedings of CVPR, pp. 74–81 (2004)Google Scholar
  9. 9.
    Chen, Y., Quan, L.: Region – Based Progressive Stereo Matching. In: Proceedings of CVPR, pp. 106–113 (2004)Google Scholar
  10. 10.
    Klaus, A., Sormann, M., Karner, K.: Segment-based Stereo Matching using Belief Propagation and a Self-adapting Dissimilarity Measure. In: Proceedings of CVPR, pp. 15–18 (2006)Google Scholar
  11. 11.
    Wang, Z., Zheng, Z.: A Region based Stereo Matching Algorithm using Cooperation optimization. In: Proceedings of International Conference on Pattern Recognition, pp. 1–8 (2008)Google Scholar
  12. 12.
    Yang, Q., Wang, L., Yang, R., Stewenius, H., Nister, D.: Stereo Matching with Color-weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling. In: Proceedings of International Conference on Pattern Recognition, pp. 347–354 (2006)Google Scholar
  13. 13.
    Carr, J.C., Beatson, R.K., Cherrie, J.B., Mitchell, T.J., Fright, W.R., McCallum, B.C., Evans, T.R.: Reconstruction and Representation of 3D Objects with Radial Basis Functions. In: Proceesings of ACM SIGGRAPH, pp. 67–76 (2001)Google Scholar
  14. 14.
    Labatut, P., Pons, J.P., Keriven, R.: Robust and Efficient Surface Reconstruction from Range Data. Computer Graphics Forum, 2275–2290 (2009)Google Scholar
  15. 15.
    Fua, P.: A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications 6(1), 35–49 (1993)CrossRefGoogle Scholar
  16. 16.
    Comaniciu, D., Meer, P.: Robust Analysis of Feature Spaces: Color Image Segmentation. In: Proceedings of CVPR, pp. 750–755 (1997)Google Scholar
  17. 17.
    Min, K., Chun, J.: Image-Based 3D Face Modeling from Stereo Images. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 410–419. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    de Araujo, A.D.G., Doria Neto, A.D., de Medeiros Martins, A.: Stereo Map Surface Calculus Optimization Using Radial Basis Functions Neural Network Interpolation. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part I. LNCS, vol. 5863, pp. 229–236. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Carr, J.C., Fright, W.R., Beatson, R.K.: Surface Interpolation with Radial Basis Functions for Medical Imaging. IEEE Transactions on Medical Imaging 16, 96–107 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaozhou Zhou
    • 1
  • Pierre Boulanger
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

Personalised recommendations