General and nested Wiberg minimization

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 1584-1591

  Strelow, Dennis ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARBITRARY FUNCTIONS; BUNDLE ADJUSTMENTS; CLOSED FORM; FACTORIZATION ALGORITHMS; MATRIX FACTORIZATIONS; PROJECTIVE DEPTH; SUCCESSIVE LINEAR PROGRAMMING;

ALGORITHMS; COMPUTER VISION; FACTORIZATION; ITERATIVE METHODS; LINEAR PROGRAMMING;

MATRIX ALGEBRA;

EID: 84866647145     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247850     Document Type: Conference Paper

References (14)

1. M. S. Bazaraa, H. D. Sherali, and C. Shetty. Nonlinear programming: theory and algorithms. Wiley, Hoboken, New Jersey, third edition, 2006.
2. A. Eriksson and A. van den Hengel. Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm. In Computer Vision and Pattern Recognition, San Francisco, CA, June 2010.
3. P. L. Fackler. Notes on matrix calculus. http://www4.ncsu.edu/-pfackler/ MatCalc.pdf, September 2005. Accessed: 08/29/2011.
4. D. A. Forsyth and J. Ponce. Computer vision: A modern approach. Prentice Hall, Upper Saddle River, New Jersey, 2003.
5. R. Hartley and A. Zisserman. Multiple view geometry in computer vision. Cambridge University Press, Cambridge, UK, second edition, 2003.
6. Q. Ke and T. Kanade. Robust L1 norm factorization in the presence of outliers and missing data by alternative convex programming. In Computer Vision and Pattern Recognition, San Diego, CA, June 2005.
7. T. Okatani and K. Deguchi. On the Wiberg algorithm for matrix factorization in the presence of missing components. International Journal of Computer Vision, 72(3), May 2007.
8. T. Okatani, T. Yoshida, and K. Deguchi. Efficient algorithm for low-rank matrix factorization with missing components and performance comparison of latest algorithms. In International Conference on Computer Vision, Barcelona, Spain, November 2011.
9. J. Oliensis and R. Hartley. Iterative extensions of the Sturm/Triggs algorithm: convergence and nonconvergence. IEEE PAMI, 29(12):2217-2233, December 2007.
10. C. Poelman. The paraperspective and projective factorization methods for recovering shape and motion. PhD thesis, Carnegie Mellon University, 1995.
11. F. Richards. A method of maximum-likelihood estimation. Journal of the Royal Statistical Society, Series B (Methodological), 23(2):469-475, 1961.
12. A. Ruhe and P. Wedin. Algorithms for separable nonlinear least squares problems. SIAM Review, 22(3):318-337, 1980.
13. D. Strelow and S. Singh. Motion estimation from image and inertial measurements. International Journal of Robotics Research, 23(12):1157-1195, December 2004.
14. T. Wiberg. Computation of principal components when data are missing. In Second Symposium of Computation Statistics, pages 229-326, Berlin, 1976.