Image Matching with Spatially Variant Contrast and Offset: A Quadratic Programming Approach

  • Alexander Shorin
  • Georgy Gimel’farb
  • Patrice Delmas
  • Jonh Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)


Template matching is widely used in machine vision, digital photogrammetry, and multimedia data mining to search for a target object by similarity between its prototype image (template) and a sensed image of a natural scene containing the target. In the real-world environment, similarity scores are frequently affected by contrast / offset deviations between the template and target signals. Most of the popular least-squares scores presume only simple smooth deviations that can be approximated with a low-order polynomial. This paper proposes an alternative and more general quadratic programming based matching score that extends the conventional least-squares framework onto both smooth and non-smooth signal deviations.


  1. 1.
    Aschwanden, P., Guggenbuhl, W.: Experimental results from a comparative study on correlation-type registration algorithms. In: Förstner, W., Ruwiedel, S. (eds.) Robust Computer Vision, pp. 268–289. Karlsruhe, Wichmann (1992)Google Scholar
  2. 2.
    Basri, R., Jacobs, D., Kemelmacher, I.: Photometric stereo with general, unknown lighting. International Journal of Computer Vision 72(3), 239–257 (2007)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Chen, C., Chen, Y.: Fast algorithm for robust template matching with m-estimators. IEEE Transactions on Signal Processing 51(1), 230–243 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Crowley, J., Martin, J.: Comparison of correlation techniques. In: Proc. International Conference on Intelligent Autonomous Systems (IAS-4), Karlsruhe, Germany, March 27–30, pp. 86–93. IOS Press, Amsterdam (1995)Google Scholar
  5. 5.
    D’Esopo, D.: A convex programming procedure. Naval Research Logistics Quarterly 6, 33–42 (1959)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fitch, A., Kadyrov, A., Christmas, W., Kittler, J.: Fast robust correlation. IEEE Transactions on Image Processing 14(8), 1063–1073 (2005)CrossRefGoogle Scholar
  7. 7.
    Hildreth, C.: A quadratic programming procedure. Naval Research Logistics Quarterly 4, 79–85 (1957)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lai, S.: Robust image matching under partial occlusion and spatially varying illumination change. Computer Vision and Image Understanding 78(1), 84–98 (2000)CrossRefGoogle Scholar
  9. 9.
    Lai, S., Fang, M.: Method for matching images using spatially-varying illumination change models. US patent 6, 621, 929 (2003)Google Scholar
  10. 10.
    MIT face database (accessed August 24, 2006),
  11. 11.
    Pizarro, D., Peyras, J., Bartoli, A.: Light-invariant fitting of active appearance models. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVIP 2008), Anchorage, Alaska, USA, pp. 1–6 (June 2008)Google Scholar
  12. 12.
    Silveira, G., Malis, E.: Real-time visual tracking under arbitrary illumination changes. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVIP 2007), Minneapolis, USA, pp. 1–6 (June 2007)Google Scholar
  13. 13.
    Tombari, F., Di Stefano, L., Mattoccia, S.: A robust measure for visual correspondence. In: Proc. 14th Int. Conf. on Image Analysis and Processing (ICIAP), Modena, Italy, 2007, pp. 376–381 (September 2007)Google Scholar
  14. 14.
    Wei, S., Lai, S.: Robust and efficient image alignment based on relative gradient matching. IEEE Trans. on Image Processing 15(10), 2936–2943 (2006)CrossRefGoogle Scholar
  15. 15.
    Yang, C., Lai, S., Chang, L.: Robust face image matching under illumination variations. EURASIP Journal on Applied Signal Processing 2004(16), 2533–2543 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Zhu, G., Zhang, S., Chen, X., Wang, C.: Efficient illumination insensitive object tracking by normalized gradient matching. IEEE Signal Processing Letters 14(12), 944–947 (2007)CrossRefGoogle Scholar
  17. 17.
    Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. IEEE Trans. on Image Processing 16(10), 2617–2628 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alexander Shorin
    • 1
  • Georgy Gimel’farb
    • 1
  • Patrice Delmas
    • 1
  • Jonh Morris
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

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