Highest accuracy fundamental matrix computation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4844 LNCS, Issue PART 2, 2007, Pages 311-321

  Sugaya, Yasuyuki ^a   Kanatani, Kenichi ^b  

^a TOYOHASHI UNIVERSITY OF TECHNOLOGY   (Japan)

^b OKAYAMA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER VISION;

A POSTERIORI CORRECTION; FUNDAMENTAL MATRIX COMPUTATION;

COMPUTATION THEORY;

EID: 38149135132     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-76390-1_31     Document Type: Conference Paper

References (14)

1. Bartoli, A., Sturm, P.: Nonlinear estimation of fundamental matrix with minimal parameters. IEEE Trans. Patt. Anal. Mach. Intell. 26(3), 426-432 (2004)
2. Chernov, N., Lesort, C.: Statistical efficiency of curve fitting algorithms. Comput. Stat. Data Anal. 47(4), 713-728 (2004)
3. Chojnacki, W., Brooks, M.J., van den Hengel, A., Gawley, D.: On the fitting of surfaces to data with covariances. IEEE Trans. Patt. Anal. Mach. Intell. 22(11), 1294-1303 (2000)
4. Chojnacki, W., Brooks, M.J., van den Hengel, A., Gawley, D.: A new constrained parameter estimator for computer vision applications. Image Vis. Comput. 22(2), 85-91 (2004)
5. Hartley, R.I.: In defense of the eight-point algorithm. IEEE Trans. Patt. Anal. Mach. Intell. 19(6), 580-593 (1997)
6. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge, UK (2000)
7. Kanatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier Science, Amsterdam, The Netherlands 1996, Dover, New York (2005)
8. Kanatani, K., Sugaya, Y.: High accuracy fundamental matrix computation and its performance evaluation. In: Proc. 17th British Machine Vision Conf., Edinburgh, UK, September 2006, vol. 1, pp. 217-226 (2006)
9. Kanatani, K., Sugaya, Y.: Extended FNS for constrained parameter estimation. In: Proc. 10th Meeting Image Recog. Understand, Hiroshima, Japan, July 2007, pp. 219-226 (2007)
10. Leedan, Y., Meer, P.: Heteroscedastic regression in computer vision: Problems with bilinear constraint. Int. J. Comput. Vision 37(2), 127-150 (2000)
11. Matei, J., Meer, P.: Estimation of nonlinear errors-in-variables models for computer vision applications. IEEE Trans. Patt. Anal. Mach. Intell. 28(10), 1537-1552 (2006)
12. Migita, T., Shakunaga, T.: One-dimensional search for reliable epipole estimation. In: Proc. IEEE Pacific Rim Symp. Image and Video Technology, Hsinchu, Taiwan, December 2006, pp. 1215-1224 (2006)
13. Sugaya, Y., Kanatani, K.: High accuracy computation of rank-constrained fundamental matrix. In: Proc. 18th British Machine Vision Conf., Coventry, UK (September 2007)
14. Zhang, Z., Loop, C.: Estimating the fundamental matrix by transforming image points in projective space. Comput. Vis. Image Understand 82(2), 174-180 (2001)