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Pose Estimation from Airborne Video Sequences Using a Structural Approach for the Construction of Homographies and Fundamental Matrices

  • Eckart Michaelsen
  • Uwe Stilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

A structural knowledge-based search method is utilized for the estimation of geometric transforms from airborne video sequences. Examples are projective planar homographies and constraints such as the fundamental matrix. These estimations are calculated from correspondences of interest points between two images. Different approaches are discussed to cope with the problem of outlier- correspondences. To ensure any-time performance the search process is implemented in a data-driven production system. The pose estimation from planar homographies is compared to estimations from fundamental matrices. A fusion of both approaches is proposed. The image processing is performed by bottom-up structural analysis using an assessment-driven control. Examples are from the thermal spectral domain.

Keywords

Interest Point Fundamental Matrix Fundamental Matrice Epipolar Constraint Random Sample Consensus 
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.
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References

  1. 1.
    Ben-Ezra, M., Peleg, S., Werman, M.: Real Time Motion Analysis with Linear Programming. CVIU 78, 32–52 (1999)Google Scholar
  2. 2.
    Chum, O., Matas, J., Obdrzalek, S.: Epipolar Geometry from Three Correspondences. In: CVWW 2003, Vatlice, Czech Republic (2003)Google Scholar
  3. 3.
    Faugeras, O.: Three-Dimensional Computer Vision. MIT Press, Mass (1993)Google Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. Assoc. Comp. Mach. 24, 381–395 (1981)MathSciNetGoogle Scholar
  5. 5.
    Foerstner, W.: A Framework for Low Level Feature Extraction. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 383–394. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  6. 6.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  7. 7.
    Holland, P.W., Welsch, R.E.: Robust regression using iteratively reweighted leastsquares. Comm. Statist. Theor. Meth. 6, 813–827 (1977)CrossRefGoogle Scholar
  8. 8.
    Jurie, F., Dhome, M.: Real Time Robust Template Matching. In: BMVC-2002, pp. 123–132 (2002)Google Scholar
  9. 9.
    Ma, Y., Soatto, S., Kosecka, J., Sastry, S.: An Invitation to 3-D Vision. Springer, Berlin (2000)Google Scholar
  10. 10.
    Michaelsen, E., Stilla, U.: Probabilistic Decisions in Production Nets: An Example from Vehicle Recognition. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 225–233. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Michaelsen, E., Stilla, U.: Good Sample Consensus Estimation of 2d-Homographies for Vehicle Movement Detection from Thermal Videos. In: Ebner, H., Hiepke, C., Mayer, H., Pakzad, K. (eds.) Photogrammetric Image Analysis PIA 2003. Intern. Arch. of Photogr. and Rem. Sens., vol. 34, Part 3/W8, pp. 125–130 (2003)Google Scholar
  12. 12.
    Michaelsen, E., Stilla, U.: Sensor Pose Inference from Airborne Videos by Decomposing Homography Estimates. Accepted for ISPRS 2004, Commission III, WG III/I (2004)Google Scholar
  13. 13.
    Torr, P.H.S., Davidson, C.: IIMSAC: Synthesis of importance sampling and random sample consensus. IEEE – PAMI 25(3), 354–364 (2003)Google Scholar
  14. 14.
    Sawhney, H.S.: Simplifying motion and structure analysis using planar parallax and image warping. In: ICPR 1994, vol. I, pp. 403–407. IEEE-Press, Los Alamitos (1994)Google Scholar
  15. 15.
    Stilla, U., Michaelsen, E., Lütjen, K.: Automatic Extraction of Buildings from Aerial Images. In: Leberl, F., Kalliany, R., Gruber, M. (eds.) Mapping Buildings, Roads and other Manmade Structures from Images, IAPR-TC7, Wien, Oldenburg, pp. 229–244 (1996)Google Scholar
  16. 16.
    Sujew, S., Ernst, I.: LUMOS Airborne Traffic Monitoring. In: Int. Workshop on Airborne Traffic Measurement, DLR, Berlin (2003)Google Scholar
  17. 17.
    Robust Estimation Library, rrel, university of Manchester (accessed December 24, 2003), http://paine.wiau.man.ac.uk/pub/doc_vxl/contrib/rpl/rrel/html/

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Eckart Michaelsen
    • 1
  • Uwe Stilla
    • 2
  1. 1.FGAN / FOMEttlingenGermany
  2. 2.Photogrm. & Rem. Sens. / TU MunichMünchenGermany

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