Monocular Point Based Pose Estimation of Artificial Markers by Using Evolutionary Computing

  • Teuvo Heimonen
  • Janne Heikkilä
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


Evolutionary computation techniques are being increasingly applied to a variety of practical and scientific problems. In this paper we present a evolutionary approach for pose estimation of a known object from one image. The method is intended to be used in pose estimation from only a few model point - image point correspondences, that is, in cases in which traditional approaches often fail.


Genetic Operator Model Point Camera Calibration Rigid Transformation Point Correspondence 
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.
    Faugeras, O.: Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press, Cambridge (1993)Google Scholar
  2. 2.
    Horaud, R., Dornaika, F., Lamiroy, B., Christy, S.: Object pose: The link between weak perspective, paraperspective and full perspective. International Journal of Computer Vision 22(2), 173–189 (1997), CrossRefGoogle Scholar
  3. 3.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Horaud, R., Conio, B., Leboulleux, O., Lacolle, B.: An analytic solution for the perspective 4-point problem. CVGIP 47, 33–44 (1989)Google Scholar
  5. 5.
    Haralick, R.M., Joo, H., Lee, C.N., Zhuang, X., Vaidya, V.G., Kim, M.B.: Pose estimation from corresponding point data. IEEE Transactions on Systems, Man and Cybernetics 19(6), 1426–1446 (1989)CrossRefGoogle Scholar
  6. 6.
    Oberkampf, D., DeMenthon, D.F., Davis, L.S.: Iterative pose estimation using coplanar feature points. Journal of Computer Vision and Image Understanding 63(3), 495–511 (1996)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(5), 441–450 (1991)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Heikkilä, J.: Geometric camera calibration using circular control points. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1066–1077 (2000)CrossRefGoogle Scholar
  9. 9.
    Ji, Q., Zhang, Y.: Camera calibration with genetic algorithms. SMC-A 31(2), 120–130 (2001)Google Scholar
  10. 10.
    Toyama, F., Shoji, K., Miyamichi, J.: Model-based pose estimation using genetic algorithm. In: ICPR ’98: Proceedings of the 14th International Conference on Pattern Recognition, vol. 1, Washington, DC, USA, p. 198. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  11. 11.
    Hati, S., Sengupta, S.: Robust camera parameter estimation using genetic algorithm. Pattern Recogn. Lett. 22(3-4), 289–298 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Rossi, C., Abderrahim, M., Díaz, J.C.: Evopose: A model-based pose estimation algorithm with correspondences determination. In: ICMA 2005: Proceedings of the IEEE International Conference on Mechatronics and Automation, Niagara Falls, Canada, Jul. 2005, pp. 1551–1556 (2005)Google Scholar
  13. 13.
    Yu, Y.K., Wong, K.H., Chang, M.M.Y.: Pose estimation for augmented reality applications using genetic algorithm. SMC-B 35(6), 1295–1301 (2005)Google Scholar
  14. 14.
    Arun, K., Huang, T., Bolstein, S.: Least-Squares Fitting of Two 3-D Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 698–700 (1987)CrossRefGoogle Scholar
  15. 15.
    Heikkilä, J.: Camera calibration toolbox for matlab (2000),

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Teuvo Heimonen
    • 1
  • Janne Heikkilä
    • 1
  1. 1.University of Oulu, Information Processing Laboratory, Linnanmaa, Po Box 4500, 90014 University of OuluFinland

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