Journal of Real-Time Image Processing

, Volume 6, Issue 4, pp 257–273 | Cite as

A real-time fuzzy hardware structure for disparity map computation

  • Christos GeorgoulasEmail author
  • Ioannis Andreadis
Original Research Paper


Stereo images acquired by a stereo camera setup provide depth estimation of a scene. Numerous machine vision applications deal with retrieval of 3D information. Disparity map recovery from a stereo image pair involves computationally complex algorithms. Previous methods of disparity map computation are mainly restricted to software-based techniques on general-purpose architectures, presenting relatively high execution time. In this paper, a new hardware-implemented real-time disparity map computation module is realized. This enables a hardware-based fuzzy inference system parallel-pipelined design, for the overall module, implemented on a single FPGA device with a typical operating frequency of 138 MHz. This provides accurate disparity map computation at a rate of nearly 440 frames per second, given a stereo image pair with a disparity range of 80 pixels and 640 × 480 pixels spatial resolution. The proposed method allows a fast disparity map computational module to be built, enabling a suitable module for real-time stereo vision applications.


FPGA-hardware implementation Fuzzy systems Real-time imaging Disparity maps Color image processing 


  1. 1.
    Howard, J.P., Rogers, B.J.: Binocular Vision and Stereopsis. Clarendon Press, Oxford (1995)Google Scholar
  2. 2.
    Bertozzi, M., Broggi, A.: GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans. Image Process. 7(1), 62–81 (1998)CrossRefGoogle Scholar
  3. 3.
    Murray, D., Little, J.J.: Using real-time stereo vision for mobile robot navigation. Auton. Robots 8(2), 161–171 (2000)CrossRefGoogle Scholar
  4. 4.
    Faugeras, O.: Three Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge, MA (1993)Google Scholar
  5. 5.
    Barnard, S.T., Thompson, W.B.: Disparity analysis of images. IEEE Trans. Pattern Anal. Mach. Intell. 2, 333–340 (1980)CrossRefGoogle Scholar
  6. 6.
    Di Stefano, L., Marchionni, M., Mattoccia, S.: A fast area-based stereo matching algorithm. Image Vis. Comput. 22(12), 983–1005 (2004)CrossRefGoogle Scholar
  7. 7.
    Muhlmann, K., Maier, D., Hesser, J., Manner, R.: Calculating dense disparity maps from color stereo images, an efficient implementation. Int. J. Comput. Vis. 47(1–3), 79–88 (2002)CrossRefGoogle Scholar
  8. 8.
    Jordan, J.R., Bovik, A.C.: Using chromatic information in edge-based stereo correspondence. Comput. Vis. Graph. Image Process. Image Underst. 54(1), 98–118 (1991)zbMATHGoogle Scholar
  9. 9.
    Baumberg, A.: Reliable feature matching across widely separated views. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 774–81 (2000)Google Scholar
  10. 10.
    Hirschmuler, H.: Improvements in real-time correlation-based stereo vision. In: Proceedings of IEEE Workshop on Stereo and Multi-Baseline Vision, pp. 141–148 (2001)Google Scholar
  11. 11.
    Georgoulas, C., Kotoulas, L., Sirakoulis, G., Andreadis, I., Gasteratos, A.: Real-time disparity map computation module. J. Microprocess. Microsyst. 32(3), 159–170 (2008)CrossRefGoogle Scholar
  12. 12.
    Darabiha, A., MacLean, W.J., Rose, J.: Reconfigurable hardware implementation of a phase-correlation stereo algorithm. J. Mach. Vis. Appl. 17(2), 116–132 (2006)CrossRefGoogle Scholar
  13. 13.
    Ambrosch, K., Kubinger, W., Humenberger, M., Steininger, A.: Flexible hardware based stereo matching. EURASIP J. Embed. Syst. (2008). doi: 10.1155/2008/386059
  14. 14.
    Woodfill, J., Herzen, B.V.: Real time stereo vision on the parts reconfigurable computer. In: 5th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, pp. 201–210 (1997)Google Scholar
  15. 15.
    Darabiha, A., Rose, J., MacLean, W.J.: Video-rate stereo depth measurement on programmable hardware. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 203–210 (2003)Google Scholar
  16. 16.
    Diaz, J., Ros, E., Pelayo, F., Ortigosa, E.M., Mota, S.: FPGA based real-time optical-flow system. IEEE Trans. Circ. Syst. Video Technol. 16(2), 274–279 (2006)CrossRefGoogle Scholar
  17. 17.
    Van De Ville, D., Nachtegael, M., Van der Weken, D., Kerre, E.E., Philips, W., Lemahieu, I.: Noise reduction by fuzzy image filtering. IEEE Trans. Fuzzy Syst. 11(4), 429–436 (2003)CrossRefGoogle Scholar
  18. 18.
    Murino, V., Castellani, U., Fusiello, A.: Disparity map restoration by integration of confidence in markov random fields models. In: Proceedings of International Conference on Image Processing, vol. 2, pp. 29–32 (2001)Google Scholar
  19. 19.
    Delva, J.G.R., Reza, A.M., Turney, R.D.: FPGA implementation of a nonlinear two dimensional fuzzy filter. In: Proceedings of IEEE Conference on Acoustics, Speech, Signal Processing, pp. 2143–2146 (1999)Google Scholar
  20. 20.
    Tolt, G., Kalaykov, I.: Fuzzy-similarity-based noise cancellation for real-time image processing. In: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, vol. 1, pp. 15–18 (2001)Google Scholar
  21. 21.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7(1), 1–13 (1975)zbMATHCrossRefGoogle Scholar
  22. 22.
    Leekwijck, W.V., Kerre, E.E.: Defuzzification: criteria and classification. Fuzzy Sets Syst. 108(2), 159–178 (1999)zbMATHCrossRefGoogle Scholar
  23. 23.
    Leeser, M., Hauck, S., Tessier, R.: Editorial: field-programmable gate arrays in embedded systems. EURASIP J. Embed. Syst. 1, 11–11 (2006)Google Scholar
  24. 24.
    Del Campo, I., Tarela, J.M.: Consequences of the digitization on the performance of a fuzzy logic controller. IEEE Trans. Fuzzy Syst. 7, 85–92 (1999)CrossRefGoogle Scholar
  25. 25.
    Gong, M., Yang, Y.H.: Near real-time reliable stereo matching using programmable graphics hardware. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 924–931 (2005)Google Scholar
  26. 26.
    Kim, J.C., Lee, K.M., Choi, B.T., Lee, S.U.: A dense stereo matching using two-pass dynamic programming with generalized ground control points. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1075–1082 (2005)Google Scholar
  27. 27.
    Gong, M., Yang, Y.H.: Real-time stereo matching using orthogonal reliability-based dynamic programming. IEEE Trans. Image Process. 16(3), 879–884 (2007)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Veksler, O.: Extracting dense features for visual correspondence with graph cuts. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 689–694 (2003)Google Scholar
  29. 29.
    Gong, M., Yang, Y.H.: Fast unambiguous stereo matching using reliability-based dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 998–1003 (2005)CrossRefGoogle Scholar
  30. 30.
    Veksler, O.: Dense features for semi-dense stereo correspondence. Int. J. Comput. Vis. 47(1–3), 247–260 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Laboratory of Electronics, Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

Personalised recommendations