Boosting Chamfer Matching by Learning Chamfer Distance Normalization

  • Tianyang Ma
  • Xingwei Yang
  • Longin Jan Latecki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)


We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer Distance (NOCD). Our experimental results demonstrate that it boosts the detection rate of the oriented chamfer distance. The simplicity and ease of training of NOCD on a small number of training samples promise that it can replace chamfer distance and oriented chamfer distance in any template matching application.


Target Object Object Detection IEEE Conf Weak Learner Cluttered Background 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Andriluka, M., Roth, S., Schiele, B.: People-Tracking-by-Detection and People-Detection-by-Tracking. In: CVPR (2008)Google Scholar
  2. 2.
    Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: Proc. 5th Int. Joint Conf. Artificial Intelligence, pp. 659–663 (1977)Google Scholar
  3. 3.
    Gavrila, D.M., Munder, S.: Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle. International Journal of Computer Vision 73(1), 41–49 (2007)CrossRefGoogle Scholar
  4. 4.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian Detection in Crowded Scenes. In: IEEE Conf. on Computer Vision and Pattern Recognition (2005)Google Scholar
  5. 5.
    Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV 2004 Workshop on Statistical Learning in Computer Vision (2004)Google Scholar
  6. 6.
    Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Model-based hand tracking using a hierarchical bayesian filter. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(9), 1372–1384 (2006)CrossRefGoogle Scholar
  7. 7.
    Opelt, A., Pinz, A., Zisserman, A.: A Boundary-Fragment-Model for Object Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Opelt, A., Pinz, A., Zisserman, A.: Incremental learning of object detectors using a visual shape alphabet. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 3–10 (2006)Google Scholar
  9. 9.
    Heitz, G., Elidan, G., Packer, B., Koller, D.: Shape-Based Object Localization for Descriptive Classification. International Journal of Computer Vision 84(1) (August 2009)Google Scholar
  10. 10.
    Van Herk, M.: Image Registration Using Chamfer Matching. In: Handbook of medical imaging: processing and analysis. Academic Press, London (2000)Google Scholar
  11. 11.
    Nomira, O., Abdel-Mottalebb, M.: Hierarchical contour matching for dental X-ray radiographs. Pattern Recognition 41(1), 130–138 (2008)CrossRefGoogle Scholar
  12. 12.
    Faisan, S., Passat, N., Noblet, V., Chabrier, R., Meyer, C.: Topology Preserving Warping of Binary Images: Application to Atlas-Based Skull Segmentation. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 211–218. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Wu, B., Nevatia, R.: Detection and Segmentation of Multiple, Partially Occluded Objects by Grouping, Merging, Assigning Part Detection Responses. International Journal of Computer Vision 82(2), 185 (2009)CrossRefGoogle Scholar
  14. 14.
    Thayananthan, A., Stenger, B., Torr, P., Cipolla, R.: Shape context and chamfer matching in cluttered scenes. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 127–133 (2003)Google Scholar
  15. 15.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(24), 509–522 (2002)CrossRefGoogle Scholar
  16. 16.
    Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Trans. Pattern Analysis and Machine Intell. 10(6), 849–865 (1988)CrossRefGoogle Scholar
  17. 17.
    Shotton, J., Blake, A., Cipolla, R.: Multi-scale categorical object recognition using contour fragments. IEEE Trans. Pattern Analysis and Machine Intell. 30(7), 1270–1281 (2008)CrossRefGoogle Scholar
  18. 18.
    Freund, Y., Schapire, R.: A decision theoretic generalisation of online learning. Computer and System Sciences 55(1), 119–139 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Zhu, L., Chen, Y., Yuille, A.: Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(1) (2009)Google Scholar
  20. 20.
    Lee, Y., Grauman, K.: Shape Discovery from Unlabeled Image Collections. In: IEEE Conf. on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 2254–2261 (June 2009)Google Scholar
  21. 21.
    Torralba, A., Murphy, K., Freeman, W.: Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 762–769 (2004)Google Scholar
  22. 22.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. on Computer Vision and Pattern Recognition (2001)Google Scholar
  23. 23.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conf. on Computer Vision and Pattern Recognition (2005)Google Scholar
  24. 24.
    Seemann, E., Schiele, B.: Cross-articulation learning for robust detection of pedestrians. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 242–252. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Trinh, N.H., Kimia, B.B.: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model. In: Proceedings of the Twentieth British Machine Vision Conference, London, UK (September 2009)Google Scholar
  26. 26.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing Images Using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)CrossRefGoogle Scholar
  27. 27.
    Olson, C.F., Huttenlocher, D.P.: Automatic Target Recognition by Matching Oriented Edge Pixels. IEEE Transactions on Image Processing 6(1), 103–113 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tianyang Ma
    • 1
  • Xingwei Yang
    • 1
  • Longin Jan Latecki
    • 1
  1. 1.Dept. of Computer and Information SciencesTemple UnviersityPhiladelphia

Personalised recommendations