Adaptive Implicit-Camera Calibration in Photogrammetry Using Anfis

  • Erkan Beṣdok
  • Pınar Çivicioğlu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


Camera Calibration (CC) is required in many Photogrammetry and Computer-Vision applications, where 3D information is extracted from images and CC is also employed for pose determination of imaging sensors. In this paper, a novel implicit-CC model (ICC) based on Adaptive Neuro Fuzzy Inference System has been introduced. The ICC is particularly useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The ICC supports multi-view back-projection in intelligent-photogrammetry. In this paper, the back-projection performance of the ICC has been compared with the Modified Direct-Linear-Transformation (MDLT) on real-images in order to evaluate the success of the proposed ICC. Extensive simulation results show that the ICC achieves a better performance than the MDLT in the 3D reconstruction of scene.


Adaptive Neuro Fuzzy Inference System Camera Calibration Triangular Membership Function Fuzzy Structure Camera Calibration Parameter 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Klir, G., Wang, Z., Harmanec, D.: Geometric Camera Calibration Using Circular Control Points. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1066–1076 (2000)CrossRefGoogle Scholar
  2. 2.
    Pan, H.P.: A Basic Theory of Photogrammetron. International Archives of Photogrammetry and Remote Sensing XXXIV (3) (2002)Google Scholar
  3. 3.
    Pan, H.P., Zhang, C.S.: System Calibration of Intelligent Photogrammetron. International Archives of Photogrammetry and Remote Sensing XXXIV (2) (2002)Google Scholar
  4. 4.
    Lynch, M.B., Dagli, C.H., Vallenki, M.: The Use of Fedforward Neural Networks for Machine Vision Calibration. Int. Journal of Production Economics, 60–61, 479-489, (1999)Google Scholar
  5. 5.
    Hatze, H.: High-Precision Three-Dimensional Photogrammetric Calibration and Object Space Reconstruction Using a Modified Dlt-Approach. J. of Biomechanics 21, 533–538 (1988)CrossRefGoogle Scholar
  6. 6.
    Ahmed, M.T., Hemayed, E., Farag, A.: Neurocalibration: A Neural Network that can Tell Camera Calibration Parameters. In: Proc. of the International Conference on Computer Vision, Korfu, Greece, vol. 1, pp. 463–468 (1999)Google Scholar
  7. 7.
    Jun, J., Choongwon, K.: Robust Camera Calibration Using Neural Network. In: IEEE Region 10 Conference TENCON 1999, vol. 1, pp. 1694-1697 (1999)Google Scholar
  8. 8.
    Lucchese, L.: Geometric Calibration of Digital Cameras through Multi-View Rectification. Image and Computing 23, 517–539 (2005)CrossRefGoogle Scholar
  9. 9.
    Jang, J.S.R.: Anfis: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Beşdok, E., Çiviciog̃lu, P., Alç\(\imath\), M.: Using an Adaptive Neuro-Fuzzy Inference System-Based Interpolant for Impulsive Noise Suppression from Highly Distorted Images. Fuzzy Sets and Systems 150, 525–543 (2005)Google Scholar
  11. 11.
    Beşdok, E., Çiviciog̃lu, P., Alç\(\imath\), M.: Using Anfis with Circular Polygons for Impulsive Noise Suppression From Highly Distorted Images, AEU-International Journal of Electronics and Communications 59 (4), 213-221 (2005)Google Scholar
  12. 12.
    Heping, P., Chusen, Z.: System Structure and Calibration Models of Intelligent Photogrammetron. Wuhan University Journal 2(48) (2003)Google Scholar
  13. 13.
    FarField Technology, FastRBF Toolbox, MATLAB Interface Version 1.4 (2004),
  14. 14.
    Mathworks Inc., Matlab Neural Networks Toolbox and Fuzzy Toolbox, Mathworks (2005)Google Scholar
  15. 15.
    Abdel-Aziz, Y.I., Karara, H.M.: Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry. In: Proceedings of the Symposium on Close-Range Photogrammetry, pp. 1–18. American Society of Photogrammetry, Falls Church (1971)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Erkan Beṣdok
    • 1
  • Pınar Çivicioğlu
    • 2
  1. 1.Enginering Faculty, Geodesy and Photogrammetry Engineering Dept.Erciyes UniversityKayseriTurkey
  2. 2.Civil Aviation School, Aircraft Electrics and Electronics Dept.Erciyes UniversityKayseriTurkey

Personalised recommendations