New algorithms for 2D and 3D point matching: Pose estimation and correspondence

Pattern Recognition

Volumn 31, Issue 8, 1998, Pages 1019-1031

  Gold, Steven ^a   Rangarajan, Anand ^b   Lu, Chien Ping ^c   Pappu, Suguna ^b   Mjolsness, Eric ^d  

^a CURAGEN CORPORATION   (United States)

^b YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c HEWLETT PACKARD ENTERPRISE   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Affine transformation; Correspondence; Deterministic annealing; Neural networks; Optimization; Point matching; Pose estimation; Softassign

Indexed keywords

ALGORITHMS; COMPUTER VISION; ESTIMATION; MATHEMATICAL TRANSFORMATIONS; OPTIMIZATION; PROBLEM SOLVING; RECURRENT NEURAL NETWORKS;

CORRESPONDENCE; DETERMINISTIC ANNEALING; POINT MATCHING; POSE ESTIMATION; SOFTASSIGN;

PATTERN MATCHING;

EID: 0032141691     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(98)80010-1     Document Type: Article

References (58)

1. E. Grimson, Object Recognition by Computer: The Role of Geometric Constraints. MIT Press, Cambridge, MA (1990).
2. J. J. Hopfield and D. Tank, 'Neural' computation of decisions in optimization problems, Biological Cybernet. 52, 141-152 (1985).
3. G. V. Wilson and G. S. Pawley, On the stability of the traveling salesman problem algorithm of Hopfield and Tank, Biological Cybernet. 58, 63-70 (1988).
4. B. Kamgar-Parsi and B. Kamgar-Parsi, On problem solving with Hopfield networks, Biological Cybernet. 62, 415-423 (1990).
5. C. Peterson and B. Soderberg, A new method for mapping optimization problems onto neural networks, Internat. J. Neural Systems 1(1), 3-22 (1989).
6. D. E. Van den Bout and T. K. Miller III, Graph partitioning using annealed networks, IEEE Trans. Neural Networks 1(2), 192-203 (1990).
7. P. D. Simic, Constrained nets for graph matching and other quadratic assignment problems, Neural Computat. 3, 268-281 (1991).
8. J. J. Kosowsky and A. L. Yuille, The invisible hand algorithm: Solving the assignment problem with statistical physics. Neural Networks 7(3), 477-490 (1994).
9. S. Gold, A. Rangarajan and E. Mjolsness, Learning with preknowledge: clustering with point and graph matching distance measures, Neural Comput. 8(4), 787-804 (1996).
10. A. Rangarajan, S. Gold and E. Mjolsness, A novel optimizing network architecture with applications, Neural Comput. 8(5), 1041-1060 (1996).
11. S. Gold and A. Rangarajan. A graduated assignment algorithm for graph matching, IEEE Trans. Pattern Anal. Mach. Intell. 18(4), 377-388 (1996).
12. S. Gold, Matching and learning structural and spatial representations with neural networks. Ph.D. thesis, Yale University (1995).
13. S. Gold and A. Rangarajan, Softassign versus softmax: Benchmarks in combinatorial optimization, in Advances in Neural Information Processing Systems , Vol. 8, D. S. Touretzky, M. C. Mozer and M. E. Hasselmo, eds., pp. 626-632. MIT Press, Cambridge, MA (1996).
14. H. Baird, Model-Based Image Matching Using Location, MIT Press, Cambridge, MA (1984).
15. E. Grimson and T. Lozano-Perez, Localizing overlapping parts by searching the interpretation tree, IEEE Trans. Pattern Anal. Mach. Intell. 9, 468-482 (1987).
16. S. Umeyama, Parameterized point pattern matching and its application to recognition of object families, IEEE Trans. Pattern Anal. Mach. Intell. 15(1), 136-144 (January 1993).
17. D. H. Ballard, Generalized hough transform to detect arbitrary patterns, IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 111-122 (1981).
18. G. Stockman, Object recognition and localization via pose clustering, Comput. Vision Graphics Image Process. 40 (1987).
19. Y. Lamdan, J. Schwartz and H. Wolfson, Object recognition by affine invariant matching, Proc. IEEE Conf. Comput. Vision Pattern Recognition, pp. 335-344 (1988).
20. R. Hummel and H. Wolfson, Affine invariant matching, Proc. DARPA Image Understanding Workshop, pp. 351-364. Cambridge, MA (1988).
21. S. Ullman, Aligning pictorial descriptions: an approach to object recognition, Cognition, 32(3), 193-254 (1989).
22. C. F. Olson, Probabilistic indexing for object recognition, IEEE Trans. Pattern Anal. Mach. Intell. 17(5), 518-521 (May 1995).
23. D. Weinshall and C. Tomasi, Linear and incremental acquisition of invariant shape models from image sequences, IEEE Trans. Pattern Anal. Mach. Intell. 17, 512-517 (May 1995).
24. D. W. Jacobs, Generalizing invariants for 3-D to 2-D matching, in Springer Lecture Notes in Computer Science, Vol. 825, J. L. Mundy, A. Zisserman and D. Forsyth, eds., pp. 415-434 (1994).
25. A. Califano and R. Mohan, Multidimensional indexing for recognizing visual shapes, IEEE Trans. Pattern Anal. Mach. Intell. 16, 373-392 (April 1994).
26. D. P. Huttenlocher, G. A. Klanderman and W. J. Rucklidge, Comparing images using the Hausdorff distance, IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (September 1993).
27. P. J. Besl and N. D. McKay, A method for registration of 3-D shapes, IEEE Trans. Pattern Anal. Mach. Intell. 14, 239-256 (February 1992).
28. G. Scott and C. Longuet-Higgins, An algorithm for associating the features of two images, Proc. Roy. Soc. London, B 244, 21-26 (1991).
29. L. Shapiro and J. Brady, Feature-based correspondence: an eigenvector approach, Image and Vision Comput. 10, 283-288 (1992).
30. S. Sclaroff and A. P. Pentland, Modal matching for correspondence and recognition, IEEE Trans. Patt. Anal. Mach. Intell. 17, 545-561 (June 1995).
31. A. L. Yuille, Generalized deformable models, statistical physics, and matching problems, Neural Comput. 2(1), 1-24 (1990).
32. A. Yuille and P. Hallinan, Deformable templates, in Active Vision, A. Blake and A. Yuille, eds., MIT Press, Cambridge, MA (1992).
33. M. Kass, A. Witkin and D. Terzopoulos, Snakes: active contour models, Internat. J. Comput. Vision 1, 321-331 (1988).
34. R. Brunelli and T. Poggio, Face recognition: Features versus templates, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1042-1052 (November 1993).
35. G. Hinton, C. Williams and M. Revow, Adaptive elastic models for hand-printed character recognition, in Advances in Neural Information Processing Systems, Vol. 4, J. Moody, S. Hanson and R. Lippmann, eds., Morgan Kaufmann, San Mateo, CA (1992).
36. S. Ranade and A. Rosenfeld, Point pattern matching by relaxation, Pattern Recognition 12, 269-275 (1980).
37. J. Ton and A. Jain, Registering Landsat images by point matching, IEEE Trans. Geo. Rem. Sens. 27, 642-651 (September 1989).
38. S. Z. Li, Matching: invariant to translations, rotations and scale changes, Pattern Recognition 25, 583-594 (1992).
39. E. Mjolsness and C. Garrett, Algebraic transformations of objective functions, Neural Networks 3, 651-669 (1990).
40. E. Mjolsness, Bayesian inference on visual grammars by neural nets that optimize. Technical Report YALEU/DCS/TR-854, Department of Computer Science, Yale University (May 1991).
41. C. P. Lu and E. Mjolsness, Two-dimensional object localization by coarse-to-fine correlation matching, in Advances in Neural Information Processing Systems, Vol. 6, J. Cowan, G. Tesauro and J. Alspector, eds., pp. 985-992. Morgan Kaufmann, San Francisco, CA (1994).
42. V. Vinod and S. Ghose, Point matching using asymmetric neural networks, Pattern Recognition 26, 1207-1214 (1993).
43. A. H. Gee, S. Aiyer and R. W. Prager, An analytical framework for optimizing neural networks, Neural Networks 6, 79-97 (1993).
44. F. Girosi, M. Jones and T. Poggio, Regularization theory and neural network architectures, Neural Comput. 7, 219-269 (1995).
45. S. Pappu, S. Gold and A. Rangarajan, A framework for nonrigid matching and correspondence, in Advances in Neural Information Processing Systems, Vol. 8, D. S. Touretzky, M. C. Mozer and M. E. Hasselmo, eds., pp. 795-801. MIT Press, Cambridge, MA (1996).
46. R. Duda and P. Hart, Pattern Classification and Scene Analysis. Wiley, New York (1973).
47. D. Geiger and F. Girosi, Parallel and deterministic algorithms from MRFs: surface reconstruction, IEEE Trans. Pattern Anal. Mach. Intell. 13, 401-412 (May 1991).
48. D. Geiger and A. L. Yuille, A common framework for image segmentation, Internat. J. Comput. Vision 6(3), 227-243 (August 1991).
49. J. S. Bridle, Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters, in Advances in Neural Information Processing Systems, Vol. 2, pp. 211-217. Morgan Kaufmann, San Mateo, CA (1990).
50. R. Sinkhorn, A relationship between arbitrary positive matrices and doubly stochastic matrices, Ann. Math. Statist. 35, 876-879 (1964).
51. C. Papadimitriou and K. Steiglitz, Combinatorial Optimization, Prentice-Hall, Englewood Cliffs, NJ (1982).
52. D. P. Bertsekas and J. N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods. Prentice-Hall, Englewood Cliffs, NJ (1989).
53. V. Chvatal, Linear Programming. W. H. Freeman, New York (1983).
54. M. Black and A. Rangarajan, On the unification of line processes, outlier rejection, and robust statistics with applications to early vision, Internat. J. Comput. Vision, 19(1), 57-91 (1996).
55. I. M. Elfadel and A. L. Yuille, Mean-field phase transitions and correlation functions for Gibbs random fields, J. Math. Imaging Vision 3(2), 167-186 (1993).
56. A. L. Yuille and J. J. Kosowsky, Statistical physics algorithms that converge, Neural Comput. 6(3), 341-356 (May 1994).
57. M. W. Walker, L. Shao and R. Volz, Estimating 3-D location parameters using dual number quaternions, Comput. Vision Graphics Image Process: Image Understanding 54, 358-367 (1991).
58. E. Mjolsness, Connectionist grammars for high-level vision, in Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, V. Honavar and L. Uhr, eds., pp. 423-451. Academic Press, San Diego, CA (1994).