Grayscale Template-Matching Invariant to Rotation, Scale, Translation, Brightness and Contrast

  • Hae Yong Kim
  • Sidnei Alves de Araújo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


In this paper, we consider the grayscale template-matching problem, invariant to rotation, scale, translation, brightness and contrast, without previous operations that discard grayscale information, like detection of edges, detection of interest points or segmentation/binarization of the images. The obvious “brute force” solution performs a series of conventional template matchings between the image to analyze and the template query shape rotated by every angle, translated to every position and scaled by every factor (within some specified range of scale factors). Clearly, this takes too long and thus is not practical. We propose a technique that substantially accelerates this searching, while obtaining the same result as the original brute force algorithm. In some experiments, our algorithm was 400 times faster than the brute force algorithm. Our algorithm consists of three cascaded filters. These filters successively exclude pixels that have no chance of matching the template from further processing.


Template matching RST-invariance segmentation-free shape recognition 


  1. 1.
    Hutchinson, S., Hager, G.D., Corke, P.I.: A tutorial on visual servo control. IEEE Trans. on Robotics and Automation 13(5), 651–670 (1996)CrossRefGoogle Scholar
  2. 2.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  3. 3.
    Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. Int. J. Comput. Vision 2(3), 283–310 (1989)CrossRefGoogle Scholar
  4. 4.
    Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2), 111–122 (1981)zbMATHCrossRefGoogle Scholar
  5. 5.
    Lamdan, Y., Wolfson, H.J.: Geometric hashing: a general and efficient model-based recognition scheme. In: Int. Conf. on Computer Vision, pp. 238–249 (1988)Google Scholar
  6. 6.
    Wolfson, H.J., Rigoutsos, I.: Geometric hashing: an overview. IEEE Computational Science & Engineering, 10–21 (October-December 1997)Google Scholar
  7. 7.
    Leung, T.K., Burl, M.C., Perona, P.: Finding faces in cluttered scenes using random labeled graph matching. In: Int. Conf. on Computer Vision, pp. 637–644 (1995)Google Scholar
  8. 8.
    Mokhtarian, F., Mackworth, A.K.: A Theory of Multi-scale, Curvature Based Shape Representation for Planar Curves. IEEE T. Pattern Analysis Machine Intelligence 14(8), 789–805 (1992)CrossRefGoogle Scholar
  9. 9.
    Kim, W.Y., Yuan, P.: A practical pattern recognition system for translation, scale and rotation invariance. In: Computer Vision and Pattern Recognition, pp. 391–396 (1994)Google Scholar
  10. 10.
    Torres-Méndez, L.A., Ruiz-Suárez, J.C., Sucar, L.E., Gómez, G.: Translation, rotation and scale-invariant object recognition. IEEE Trans. Systems, Man, and Cybernetics - part C: Applications and Reviews 30(1), 125–130 (2000)CrossRefGoogle Scholar
  11. 11.
    Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Trans. Inform. Theory 1(8), 179–187 (1962)Google Scholar
  12. 12.
    Teh, C.H., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. on Pattern Analysis and Machine Intelligence 10(4), 496–513 (1988)zbMATHCrossRefGoogle Scholar
  13. 13.
    Li, J.H., Pan, Q., Cui, P.L., Zhang, H.C., Cheng, Y.M.: Image recognition based on invariant moment in the projection space. In: Int. Conf. Machine Learning and Cybernetics, Shangai, vol. 6, pp. 3606–3610 (August 2004)Google Scholar
  14. 14.
    Flusser, J., Suk, T.: Rotation moment invariants for recognition of symmetric objects. IEEE T. Image Processing 15(12), 3784–3790 (2006)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Dionisio, C.R.P., Kim, H.Y.: A supervised shape classification technique invariant under rotation and scaling. In: Int. Telecommunications Symposium, pp. 533–537 (2002)Google Scholar
  16. 16.
    Tao, Y., Ioerger, T.R., Tang, Y.Y.: Extraction of rotation invariant signature based on fractal geometry. IEEE Int. Conf. Image Processing 1, 1090–1093 (2001)Google Scholar
  17. 17.
    Ullah, F., Kaneko, S.: Using orientation codes for rotation-invariant template matching. Pattern Recognition 37, 201–209 (2004)zbMATHCrossRefGoogle Scholar
  18. 18.
    Tsai, D.M., Tsai, Y.H.: Rotation-invariant pattern matching with color ring-projection. Pattern Recognition 35, 131–141 (2002)zbMATHCrossRefGoogle Scholar
  19. 19.
    Chang, D.H., Hornak, J.P.: Fingerprint recognition through circular sampling. The Journal of Imaging Science and Technology 44(6), 560–564 (2000)Google Scholar
  20. 20.
    Tao, Y., Tang, Y.Y.: The feature extraction of chinese character based on contour information. In: Int. Conf. Document Analysis Recognition (ICDAR), pp. 637–640 (September 1999)Google Scholar
  21. 21.
    Bresenham, J.E.: A linear algorithm for incremental digital display of circular arcs. Comm. ACM 20(2), 100–106 (1977)zbMATHCrossRefGoogle Scholar
  22. 22.
    Bresenham, J.E.: Algorithm for computer control of a digital plotter. IBM Systems Journal 4(1), 25–30 (1965)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hae Yong Kim
    • 1
  • Sidnei Alves de Araújo
    • 1
  1. 1.Escola Politécnica, Universidade de São PauloBrazil

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