Procrustes alignment with the EM algorithm

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

Volumn 1689, Issue , 1999, Pages 623-631

  Luo, Bin ^a,b   Hancock, Edwin R ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

^b ANHUI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALIGNMENT; CLUTTER (INFORMATION THEORY); GAUSSIAN DISTRIBUTION; GEOMETRY; MAXIMUM LIKELIHOOD ESTIMATION; SENSITIVITY ANALYSIS; SINGULAR VALUE DECOMPOSITION;

CORRELATION MATRIX; EM ALGORITHMS; GAUSSIAN MIXTURE MODEL; POINT SET; POINT SET ALIGNMENT; PROCRUSTES; PROCRUSTES ANALYSIS;

IMAGE ANALYSIS;

EID: 84957358134     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48375-6_74     Document Type: Conference Paper

References (19)

1. N. Ahuja, Dot Pattern Processing using Voronoi Neighbourhoods, IEEE PAMI, 4(1982), 336-343.
2. N. Ahuja and B. An and B. Schachter, Image Representation using Voronoi Tessel- lation, CVGIP, 29(1985), 286-295.
3. N. Ahuja and M. Tuceryan, Extraction of Early Perceptual Structure in Dot Pat- terns: Integrating Region, Boundary and Component Gestalt, CVGIP, 48(1989), 304-356.
4. Amit, Y. and Kong, A., Graphical Templates For Model Registration, PAMI, 18(1996), 225-236.
5. T.F Cootes and C.J. Taylor and D.H. Cooper and J. Graham, Active Shape Models - Their Training and Application, CVIU, 61(1995), 38-59.
6. A.P. Dempster and N.M. Laird and D.B. Rubin, Maximum-likelihood from incom- plete data via the EM algorithm, J. Royal Statistical Soc. Ser. B (methodological), 39(1977), 1-38.
7. O.D. Faugeras and E. Le Bras-Mehlman and J-D. Boissonnat, Representing Stereo Data with the Delaunay Triangulation, Artificial Intelligence, 44(1990), 41-87.
8. D. G. Kendall, Shape manifolds,Procrustean metrics, and complex projective spaces, Bulletin of the London Mathematical Society, 16(1984), 81-121.
9. M. Lades and J.C. Vorbruggen and J. Buhmann and J. Lange and C. von der Maalsburg and R.P. Wurtz and W. Konen, Distortion-invariant object-recognition in a dynamic link architecture, IEEE Transactions on Computers, 42(1993), 300-311.
10. S. Sclaro_ and A.P. Pentland, Modal Matching for Correspondence and Recogni- tion, IEEE PAMI, 17(1995), 545-661.
11. G.L. Scott and H.C. Longuet-Higgins, An Algorithm for Associating the Features of 2 Images, Proceedings of the Royal Society of London Series B-Biological, 244(1991), 21-26.
12. L.S. Shapiro and J. M. Brady, Feature-based Correspondence - An Eigenvector Approach, IVC, 10(1992), 283-288.
13. L.S. Shapiro and J. M. Brady, Rejecting Outliers and Estimating Errors in an Orthogonal-regression Framework, Phil. Trans. Roy. Soc. A, 350(1995), 403-439.
14. M. Tuceryan and T Chorzempa, Relative Sensitivity of a Family of Closest Point Graphs in Computer Vision Applications, Pattern Recognition, 25(1991), 361-373.
15. S. Ullman, The Interpretation of Visual Motion, MIT Press,(1979).
16. S. Umeyama, An Eigen Decomposition Approach to Weighted Graph Matching Problems, IEEE PAMI, 10(1988), 695-703.
17. S. Umeyama, Least Squares Estimation of Transformation Parameters between Point sets, IEEE PAMI, 13(1991), 376-380.
18. S. Umeyama, Parameterised Point Pattern Matching and its Application to Reco- gnition of Object Families, IEEE PAMI, 15(1993), 136-144.
19. R.C. Wilson and E.R. Hancock, Structural Matching by Discrete Relaxation, IEEE PAMI, 19(1997), 634-648.