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Sensitivity Analysis of Projective Geometry 3D Reconstruction

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Performance Characterization in Computer Vision

Part of the book series: Computational Imaging and Vision ((CIVI,volume 17))

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Abstract

One of the most powerful methods for 3D scene reconstruction without camĀ­era calibration is based on Projective Geometry (Coxeter, 1994; Faugeras, 1992; Mohr and Arbogast, 1991) This method is very elegant and relies on the solution of a series of non-linear equations that allow the determination of the 3D coordinates of a point given the identity and 3D position of at least 6 reference points relying on two intersecting planes. The method is an ideal testbed for application of the variance propagation methodology for performance evaluation proposed by Haralick (1994).

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References

  • Coxeter, H.S.M. (1974) Projective Geometry,University of Toronto Press.

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  • Faugeras, O.D. (1992) What can be seen in 3D with an uncalibrated stereo rig? Second European Conference on Computer Vision, Italy, 563-578.

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  • Georgis, N., Petrou, M. and Kittler, J. (1998) Error guided design of a 3d vision system, IEEE PA MI, 20: 366 - 379.

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  • Haralick, R.M. (1994) Propagating covariance in computer vision, Proc. 12th Int. Conf. on Pattern Recognition,Jerusalem, 1:493-498.

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  • Mohr, R. and Arbogast, E. (1991) It can be done without camera calibration, Pattern Recognition Letters, 12: 39 - 43.

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Ā© 2000 Springer Science+Business Media Dordrecht

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Petrou, M., Georgis, N., Kittler, J. (2000). Sensitivity Analysis of Projective Geometry 3D Reconstruction. In: Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds) Performance Characterization in Computer Vision. Computational Imaging and Vision, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9538-4_20

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  • DOI: https://doi.org/10.1007/978-94-015-9538-4_20

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5487-6

  • Online ISBN: 978-94-015-9538-4

  • eBook Packages: Springer Book Archive

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