International Journal of Computer Vision

, Volume 47, Issue 1–3, pp 229–246 | Cite as

Real-Time Correlation-Based Stereo Vision with Reduced Border Errors

  • Heiko Hirschmüller
  • Peter R. Innocent
  • Jon Garibaldi


This paper describes a real-time stereo vision system that is required to support high-level object based tasks in a tele-operated environment. Stereo vision is computationally expensive, due to having to find corresponding pixels. Correlation is a fast, standard way to solve the correspondence problem. This paper analyses the behaviour of correlation based stereo to find ways to improve its quality while maintaining its real-time suitability. Three methods are suggested. Two of them aim to improve the disparity image especially at depth discontinuities, while one targets the identification of possible errors in general. Results are given on real stereo images with ground truth. A comparison with five standard correlation methods is provided. All proposed algorithms are described in detail and performance issues and optimisation are discussed. Finally, performance results of individual parts of the stereo algorithm are shown, including rectification, filtering andcorrelation using all proposed methods. The implemented system shows that errors of simple stereo correlation, especially in object border regions, can be reduced in real-time using non-specialised computer hardware.

stereo vision real-time correlation problems multiple correlation windows object border correction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Boykov, Y., Veksler, O., and Zabih, R. 1998. A variable window approach to early vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:1283–1294.Google Scholar
  2. Cox, I.J., Hingorani, S.L., Rao, S.B., and Maggs, B.M. 1996. A maximum likelihood stereo algorithm. Computer Vision and Image Understanding, 63:542–567.Google Scholar
  3. Faugeras, O., Hotz, B., Mathieu, H., Viville, T., Zhang, Z., Fua, P., Thron, E., Moll, L., Berry, G., Vuillemin, J., Bertin, P., and Proy, C. 1993. Real time correlation-based stereo: Algorithm, implementations and application. INRIA, Tech. Rep. 2013.Google Scholar
  4. Fua, P. 1993. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, 6:35–49.Google Scholar
  5. Fusiello, A., Roberto, V., and Trucco, E. 1997. Efficient stereo with multiple windowing. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Puerto Rico. IEEE: Piscataway, NJ, pp. 858–863.Google Scholar
  6. Kanade, T. and Okutomi, M. 1994. Astereo matching algorithm with an adaptive window: Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:920.Google Scholar
  7. Konolige, K. 1997. Small vision systems: Hardware and implementation. In Eighth International Symposium on Robotics Research, Hayama, Japan. Springer: London, pp. 203–212.Google Scholar
  8. Little, J.J. 1992. Accurate early detection of discontinuities. In Proceedings of the Vision Interface Conference VI'92, Vancouver, Canada, pp. 97–102.Google Scholar
  9. Marr, D. and Poggio, T. 1979. A computational theory of human stereo vision. Proceedings of the Royal Society, B-204:301–328.Google Scholar
  10. Matthies, L., Kelly, A., and Litwin, T. 1995. Obstacle detection for unmanned ground vehicles: A progress report. In International Symposium of Robotics Research, Munich, Germany.Google Scholar
  11. Moravec, H. 1997. Toward automatic visual obstacle avoidance. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, pp. 584–590.Google Scholar
  12. Szeliski, R. and Zabih, R. 1999. An experimental comparison of stereo algorithms. In Vision Algorithms: Theory and Practice, Springer-Verlag: Corfu, Greece, pp. 1–19.Google Scholar
  13. Volpe, R., Balaram, J., Ohm, T., and Ivlev, R. 1996. The rocky 7 mars rover prototype. In International Conference on Intelligent Robots and Systems, Osaka, Japan, vol. 3, pp. 1558–1564.Google Scholar
  14. Zabih, R. and Woodfill, J. 1994. Non-parametric local transforms for computing visual correspondance. In Proceedings of the European Conference of Computer Vision 94, pp. 151–158.Google Scholar
  15. Zitnick, C. and Kanade, T. 1999. A cooperative algorithm for stereo matching and occlusion detection. Carnegie Mellon University, Robotics Institute, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-99-35.Google Scholar
  16. Zitnick, C.L. and Kanade, T. 2000. Acooperative algorithm for stereo matching and occlusion detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:675–684.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Heiko Hirschmüller
    • 1
  • Peter R. Innocent
    • 1
  • Jon Garibaldi
    • 1
  1. 1.Centre for Computational IntelligenceDe Montfort UniversityLeicesterUK

Personalised recommendations