Unbiased Errors-In-Variables Estimation Using Generalized Eigensystem Analysis

1. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 1st edn. Cambridge University Press, Cambridge (2000)

   MATH  Google Scholar 

2. Abatzoglou, T., Mendel, J., Harada, G.: The constrained total least squares technique and its applications to harmonic superresolution. IEEE Transactions on Signal Processing 3, 1070–1087 (1991)

   Article  MATH  Google Scholar 

3. van Huffel, S., Vandewalle, J.: The Total Least Squares problem: Computational aspects and analysis. SIAM (Society for Industrial and Applied Mathematics), Philadelphia (1991)

   Book  MATH  Google Scholar 

4. Irani, M., Anandan, P.: Factorization with uncertainty. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 539–553. Springer, Heidelberg (2000)

   Chapter  Google Scholar 

5. Matei, B., Meer, P.: A general method for errors-in-variables problems in computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 18–25 (2000)

   Google Scholar 

6. Nestares, O., Fleet, D.J., Heeger, D.J.: Likelihood functions and confidence bounds for Total Least Squares estimation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2000), Hilton Head, pp. 523–530 (2000)

   Google Scholar 

7. Kanatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier, Amsterdam (1996)

   MATH  Google Scholar 

8. Chojnacki, W., Brooks, M.J., van den Hengel, A., Gawley, D.: On the fitting of surfaces to data with covariances. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1294–1303 (2000)

   Article  Google Scholar 

9. Chojnacki, W., Brooks, M.J., van den Hengel, A., Gawley, D.: A new approach to constrained parameter estimation applicable to some computer vision problems. In: Suter, D. (ed.) Proceedings of the Statistical Methods in Video Processing Workshop (2002)

   Google Scholar 

10. Ng, L., Solo, V.: Errors-in-variables modeling in optical flow estimation. IEEE Transactions on Image Processing 10, 1528–1540 (2001)

    Article  MATH  Google Scholar 

11. Stewart, G.W.: Stochastic perturbation theory. SIAM Review 32, 576–610 (1990)

    Article  MathSciNet  Google Scholar 

12. Mühlich, M., Mester, R.: A considerable improvement in non-iterative homography estimation using TLS and equilibration. Pattern Recognition Letters 22 (2001)

    Google Scholar 

13. Mühlich, M., Mester, R.: The role of total least squares in motion analysis. In: Proc. Europ. Conf. Comp. Vision (1998)

    Google Scholar 

14. Mühlich, M., Mester, R.: Subspace methods and equilibration in computer vision. Technical Report XP-TR-C-21, J.W.G. University Frankfurt (1999)

    Google Scholar 

15. Halíř, R., Flusser, J.: Numerically stable direct least squares fitting of ellipses. In: Skala, V. (ed.) Proc. Int. Conf. in Central Europe on Computer Graphics, Visualization and Interactive Digital Media (WSCG 1998), pp. 125–132 (1998)

    Google Scholar 

Download references