Matching Occluded Objects Invariant to Rotations, Translations, Reflections, and Scale Changes

  • Yasser El-Sonbaty
  • M. A. Ismail
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


In this paper, a new algorithm for recognizing partially occluded objects is introduced. The proposed algorithm is based on searching for first three matched connected lines in both occluded and model objects, then left and right lines in both occluded and model objects are marked as matched lines as long as they have the same relations of distance ratio and angle to the last matched and connected lines. The process is repeated until there is no more three matched connected lines. The ratio_test is then performed to detect scattered matched points and lines. The new algorithm is invariant to translations, rotations, reflections and scale changes and has computational complexity of O(m.n).


  1. 1.
    Tsang P., Yuen P., Lam F.: Classification of Partially Occluded Objects Using Three Point matching and Distance Transformation. Pattern Recognition (1994) Vol. 27, 27–40.CrossRefGoogle Scholar
  2. 2.
    Bir Bhanu, Ming J. C.: Recognition of Occluded Objects: A Cluster-Structure Algorithm. Pattern Recognition (1987) Vol. 20, No. 2, 199–211.CrossRefGoogle Scholar
  3. 3.
    Liu H. C., Srinath M. D.: Partial Shape Classification Using Contour Matching in Distance Transformation. IEEE Trans. On PAMI (1990), Vol. 12, No. 11, 1072–1079.Google Scholar
  4. 4.
    Lamdan Y., Schwartz J. T., Wolfson H. J.: Affine Invariant Model-based Object Recognition. IEEE Trans. On Robotics Automn (1990), Vol 6, No. 5.Google Scholar
  5. 5.
    Sethi I., Ramesh N.: Local Association Based Recognition of Two Dimensional Objects. Machine Vision and Applications (1992.)Google Scholar
  6. 6.
    Pikaz, Dinstein I.: Matching of Partially Occluded Planer Curves. Pattern Recognition (1995), Vol. 28, No. 2, 199–209.CrossRefGoogle Scholar
  7. 7.
    Price K.: Matching Closed Contours. Proc. IEEE Workshop on Computer Vision (1984) 130–134.Google Scholar
  8. 8.
    Bhanu B., Faugeras O. D.: Shape Matching of Two Dimensional Objects. IEEE Trans. On PAMI (1984) Vol. 6, 137–156.Google Scholar
  9. 9.
    Min-Hong Han, Dongsig J.: The Use of Maximum Curvature Points for the Recognition of Partially Occluded Objects. Pattern Recognition (1990), Vol. 23, No. 1, 21–33.CrossRefGoogle Scholar
  10. 10.
    Hong, Tan X.: A New Approach to Point Pattern Matching. Proc. 9th Int. Conf. Pattern Recognition (1988) 82–84.Google Scholar
  11. 11.
    Kurita T., Takahashi T., Ikeda Y.: A Neural Network Classifier for Occluded Images. 16th Int. Conf. on Pattern Recognition (2002) 45–48.Google Scholar
  12. 12.
    Koch M. W., Kashyap R. L., “Using Polygons to Recognize and Locate Partially Occluded Objects”, IEEE Trans. On PAMI, Vol. 9, P. 483–494, 1987.Google Scholar
  13. 13.
    Bhanu B., Ming J. C.: Recognition of Occluded Objects: A Cluster Structure Algorithm. Pattern Recognition (1987), Vol. 20, 199–211.CrossRefGoogle Scholar
  14. 14.
    Petrakis E. G. M., Diplaros A., Milios E.: Matching and Retrieval of Distorted and Occluded Shapes using Dynamic Programming. IEEE Trans. On PAMI (2002)1501–1516.Google Scholar
  15. 15.
    Chang S., Hsuan F., Hsu W., Wu G.: Fast Algorithm for Point Pattern Matching: Invariant to Translations, Rotations and Scale Changes. Pattern Recognition (1997) 311–320.Google Scholar
  16. 16.
    Hu J., Yan H.: Polygonal Approximation of Digital Curves Based on the Principles of Perceptual Organization. Pattern Recognition (1997) Vol. 30, No. 5, 701–718.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yasser El-Sonbaty
    • 1
  • M. A. Ismail
    • 2
  1. 1.Dept. of Computer Eng.Arab Academy for Sc. and Tech.AlexandriaEgypt
  2. 2.Dept. of Computer Sci.Faculty of EngineeringAlexandriaEgypt

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