PERFORMANCE EVALUATION OF ROBUST MATCHING MEASURES

Federico Tombari, Luigi Di Stefano, Stefano Mattoccia, Angelo Galanti

2008

Abstract

This paper is aimed at evaluating the performances of different measures which have been proposed in literature for robust matching. In particular, classical matching metrics typically employed for this task are considered together with specific approaches aiming at achieving robustness. The main aspects assessed by the proposed evaluation are robustness with respect to photometric distortions, noise and occluded patterns. Specific datasets have been used for testing, which provide a very challenging framework for what concerns the considered disturbance factors and can also serve as testbed for evaluation of future robust visual correspondence measures.

References

  1. Aschwanden, P. and Guggenbuhl, W. (1992). Experimental results from a comparative study on correlation-type registration algorithms. In Forstner, W. and Ruwiedel, S., editors, Robust computer vision, pages 268-289. Wichmann.
  2. Bhat, D. and Nayar, S. (1998). Ordinal measures for image correspondence. IEEE Trans. Pattern Recognition and Machine Intelligence, 20(4):415-423.
  3. Chambon, S. and Crouzil, A. (2003). Dense matching using correlation: new measures that are robust near occlusions. In Proc. British Machine Vision Conference (BMVC 2003), volume 1, pages 143-152.
  4. Crouzil, A., Massip-Pailhes, L., and Castan, S. (1996). A new correlation criterion based on gradient fields similarity. In Proc. Int. Conf. Pattern Recognition (ICPR), pages 632-636.
  5. Fitch, A. J., Kadyrov, A., Christmas, W. J., and J, K. (2002). Orientation correlation. In Rosin, P. and Marshall, D., editors, British Machine Vision Conference, volume 1, pages 133-142.
  6. Giachetti, S. (2000). Matching techniques to compute image motion. Image and Vision Computing, 18:247260.
  7. Kaneko, S., Satoh, Y., and Igarashi, S. (2003). Using selective correlation coefficient for robust image registration. Journ. Pattern Recognition, 36(5):1165-1173.
  8. Martin, J. and Crowley, J. (1995). Experimental comparison of correlation techniques. In Proc. Int. Conf. on Intelligent Autonomous Systems, volume 4, pages 86- 93.
  9. Scharstein, D. (1994). Matching images by comparing their gradient fields. In Proc. Int. Conf. Pattern Recognition (ICPR), volume 1, pages 572-575.
  10. Seitz, P. (1989). Using local orientational information as image primitive for robust object recognition. In Proc. SPIE, Visual Communication and Image Processing IV, volume 1199, pages 1630-1639.
  11. Shen, J. and Castan, S. (1992). An optimal linear operator for step edge detection. Graphical Models and Image Processing (CVGIP), 54(2):112-133.
  12. Tombari, F., Di Stefano, L., and Mattoccia, S. (2007). A robust measure for visual correspondence. In Proc. 14th Int. Conf. on Image Analysis and Processing (ICIAP 2007), pages 376-381.
  13. Ullah, F., Kaneko, S., and Igarashi, S. (2001). Orientation code matching for robust object search. IEICE Trans. Information and Systems, E-84-D(8):999-1006.
  14. Zabih, R. and Woodfill, J. (1994). Non-parametric local transforms for computing visual correspondence. In Proc. European Conf. Computer Vision, pages 151- 158.
  15. Zitová, B. and Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21(11):977-1000.
Download


Paper Citation


in Harvard Style

Tombari F., Di Stefano L., Mattoccia S. and Galanti A. (2008). PERFORMANCE EVALUATION OF ROBUST MATCHING MEASURES . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 473-478. DOI: 10.5220/0001087304730478


in Bibtex Style

@conference{visapp08,
author={Federico Tombari and Luigi Di Stefano and Stefano Mattoccia and Angelo Galanti},
title={PERFORMANCE EVALUATION OF ROBUST MATCHING MEASURES},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={473-478},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001087304730478},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - PERFORMANCE EVALUATION OF ROBUST MATCHING MEASURES
SN - 978-989-8111-21-0
AU - Tombari F.
AU - Di Stefano L.
AU - Mattoccia S.
AU - Galanti A.
PY - 2008
SP - 473
EP - 478
DO - 10.5220/0001087304730478