Real-Time Scan-Line Segment Based Stereo Vision for the Estimation of Biologically Motivated Classifier Cells

  • M. Salah E. -N. Shafik
  • Bärbel Mertsching
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5803)


In this paper we present a real-time scan-line segment based stereo vision for the estimation of biologically motivated classifier cells in an active vision system. The system is challenged to overcome several problems in autonomous mobile robotic vision such as the detection of incoming moving objects and estimating its 3D motion parameters in a dynamic environment. The proposed algorithm employs a modified optimization module within the scan-line framework to achieve valuable reduction in computation time needed for generating real-time depth map. Moreover, the experimental results showed high robustness against noises and unbalanced light condition in input data.


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  1. 1.
    Shafik, M., Mertsching, B.: Enhanced motion parameters estimation for an active vision system. Pattern Recognition and Image Analysis 18(3), 370–375 (2008)CrossRefGoogle Scholar
  2. 2.
    Aziz, Z., Mertsching, B.: Fast and robust generation of feature maps for region-based visual attention. IEEE Trans. on Image Processing (5), 633–644 (2008)Google Scholar
  3. 3.
    Massad, A., Jesikiewicz, M., Mertsching, B.: Space-variant motion analysis for an active-vision system. In: Advanced Concepts for Intelligent Vision Systems, Ghent, Belgium (2002)Google Scholar
  4. 4.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. Journal of Computer Vision 47(1/2/3), 7–42 (2002)CrossRefMATHGoogle Scholar
  5. 5.
    Lei, C., Selzer, J., Yang, Y.: Region-tree based stereo using dynamic programming optimization. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition 2, 2378–2385 (2006)Google Scholar
  6. 6.
    Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: 18th Int. Conf. on Pattern Recognition, ICPR 2006, vol. 3, pp. 15–18 (2006)Google Scholar
  7. 7.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proc. Int. Conf. Computer Vision (ICCV), pp. 508–515 (2001)Google Scholar
  8. 8.
    Hirschmuller, H.: Stereo vision in structured environments by consistent semi-global matching. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. (2), June 2006, pp. 2386–2393 (2006)Google Scholar
  9. 9.
    Deng, Y., Lin, X.: A fast line segment based dense stereo algorithm using tree dynamic programming. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 201–212. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Tombari, F., Mattoccia, S., Stefano, L.D., Addimanda, E.: Classification and evaluation of cost aggregation methods for stereo correspondence. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Florida, USA (December 2008)Google Scholar
  11. 11.
    Foggia, P., Limongiello, A., Vento, M.: A real-time stereo-vision system for moving object and obstacle detection in avg and amr applications. In: Proc. of the Seventh Int. Workshop on Computer Architecture for Machine Perception (CAMP), Washington, DC, USA, pp. 58–63 (2005)Google Scholar
  12. 12.
    Yang, Q., Engels, C., Akbarzadeh, A.: Near real-time stereo for weakly-textured scenes. In: British Machine Vision Conference (BMVC), Leeds, UK (2008)Google Scholar
  13. 13.
    Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., Nistér, D.: Real-time global stereo matching using hierarchical belief propagation. In: BMVC, pp. 989–998 (2006)Google Scholar
  14. 14.
    Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Int. Symposium on 3D Data Processing Visualization and Transmission, pp. 798–805 (2006)Google Scholar
  15. 15.
    Mattoccia, S., Tombari, F., Stefano, L.D.: Stereo vision enabling precise border localization within a scanline optimization framework. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 517–527. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Albright, T.D., Desimone, R., Gross, C.G.: Columnar organisation of directionally selective cells in visual area mt of the macaque. J. Neurophysiol 51, 16–31 (1984)Google Scholar
  17. 17.
    Duffy, C.J., Wurtz, R.H.: Sensitivity of mst neurons to optic flow stimuli. i. a continuum of response selektivity to large-field stimuli. J. Neurophysiol. 65, 1329–1345 (1991)Google Scholar
  18. 18.
    Woodbeck, K., Roth, G., Chen, H.: Visual cortex on the GPU: Biologically inspired classifier and feature descriptor for rapid recognition. In: CVPRW, June 2008, pp. 1–8 (2008)Google Scholar
  19. 19.
    Mertsching, B., Aziz, Z., Stemmer, R.: Design of a simulation framework for evaluation of robot vision and manipulation algorithms. In: Proceedings of Asia Simulation Conference (ICSC), pp. 494–498 (2005)Google Scholar
  20. 20.
    Kutter, O., Hilker, C., Simon, A., Mertsching, B.: Modeling and simulating mobile robots environments. In: 3rd Int. Conf. on Computer Graphics Theory and Applications, Funchal, Madeira, Portugal (January 2008)Google Scholar
  21. 21.
    Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, June 2003, vol. 1, pp. 195–202 (2003)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. Salah E. -N. Shafik
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET LabUniversity of PaderbornPaderbornGermany

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