Recovery of global nonrigid motion: A model-based approach without point correspondences

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volumn 17, Issue 9, 2000, Pages 1617-1626

  Kumar, Senthil ^a   Goldgof, Dmitry ^b  

^a LUCENT TECHNOLOGIES   (United States)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER GRAPHICS; MATHEMATICAL TRANSFORMATIONS; POLYNOMIALS;

DATASETS;

MAMMOGRAPHY;

EID: 0038779779     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.17.001617     Document Type: Article

References (21)

1. L. Brown, “A survey of image registration techniques,” ACM (Assoc. Comput. Mach.) Comput. Surv. 24, 325-376 (1992).
2. G. Wolberg, Digital Image Warping (IEEE Computer Society Press, Los Alamitos, Calif., 1990).
3. C. Kambhamettu and D. Goldgof, “Curvature-based approach to point correspondence recovery in conformai nonrigid motion,” Comput. Vision Graph. Image Process. Image Understand. 60, 26-43 (1994).
4. C. Kambhamettu and D. Goldgof, “Point correspondence recovery in nonrigid motion,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1992), pp. 222-227.
5. R. Jain and I. K. Sethi, “Establishing correspondence of non-rigid objects using smoothness of motion,” in Proceedings of the IEEE Workshop on Computer Vision and Robotics Control (IEEE Computer Society Press, Los Alamitos, Calif., 1984), pp. 83-87.
6. S. Kumar, M. Sallam, D. Goldgof, and K. Bowyer, “Establishing point correspondences in unstructured nonrigid motion,” in Proceedings of the IEEE International Symposium on Computer Vision (IEEE Computer Society Press, Los Alamitos, Calif., 1995), pp. 289-294.
7. A. Hanson, “Hyperquadrics: Smoothly deformable shapes with convex polyhedral bounds,” Comput. Vision Graph. Image Process. 44, 191-210 (1988).
8. S. Kumar and D. Goldgof, “A robust technique for the estimation of hyperquadrics parameters from range data,” in Proceedings of the 12th International Conference on Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1994), Vol. I, pp. 74-78.
9. S. Kumar, S. Han, D. Goldgof, and K. Bowyer, “On recovering hyperquadrics from range data,” IEEE Trans. Pattern Anal. Mach. Intell. 17, 1079-1083 (1995).
10. I. Cohen and L. D. Cohen, “A hybrid hyperquadric model for 2-D and 3-D data fitting,” in Tech. Rep. 2188 (Institut National de Recherche en Informatique et d’ Automatique, Sophia Antipolis, France, 1994).
11. I. Cohen and L. D. Cohen, “A hyperquadric model for 2-D and 3-D data fitting,” in Proceedings of the 12th International Conference on Pattern Recognition (IEEE Computer Society Press, Los Alamitos Calif., 1994), Vol. II, pp. 403405.
12. C. W. Chen, T. S. Huang, and M. Arrott, “Modeling, analysis and visualization of left ventricle shape and motion by hierarchical decomposition,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 342-356 (1994).
13. A. P. Pentland, “Automatic extraction of deformable part models,” Int. J. Comput. Vis. 4, 107-126 (1990).
14. M. Sallam and K. W. Bowyer, “Registering time sequences of mammograms using a two-dimensional image unwarp-ing technique,” in Proceedings of the Second International Workshop on Digital Mammography (Elsevier, Amsterdam, 1994), pp. 121-130.
15. F. L. Bookstein, “Principal warps: Thin plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567-585 (1989).
16. S. Han and D. Goldgof, “Using hyperquadrics for shape recovery from range data,” in Proceedings of the Fourth IEEE International Conference on Computer Vision (IEEE Computer Society Press, Los Alamitos, Calif., 1993), pp. 492-496.
17. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, 2nd ed. (Cambridge U. Press, New York, 1992).
18. D. G. Luenberger, Linear and Nonlinear Programming (Addison-Wesley, Reading, Mass., 1984).
19. S. Chaudhuri and S. Chatterjee, “Estimation of motion parameters for a deformable object from range data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1989), pp. 291-295.
20. L. Stark, “Recognizing object function through reasoning about 3-d shape and dynamic physical properties,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1994), pp. 546-553.
21. M. Sutton, L. Stark, and K. W. Bowyer, “GRUFF-3: Generalizing the domain of function-based recognition system,” Pattern Recogn. 27, 1743 -1766 (1994).