A non-iterative probabilistic method for contextual correspondence matching

Pattern Recognition

Volumn 31, Issue 10, 1998, Pages 1455-1468

  Kittler, Josef ^a   Petrou, Maria ^a   Christmas, W J ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

Bayesian reasoning; Matching; Object recognition; Probabilistic reasoning; Real time implementation

Indexed keywords

ALGORITHMS; COMPUTER VISION; GRAPH THEORY; PATTERN MATCHING; PROBABILITY;

BAYESIAN REASONING; NON ITERATIVE PROBABILISTIC REASONING;

OBJECT RECOGNITION;

EID: 0032188433     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(98)00142-3     Document Type: Article

References (41)

1. E. Bienenstock, Neural-like graph-matching techniques for image processing, in Neural Information Processing Systems, D. Z. Anderson, ed., pp. 211-235. Addison-Wesley, Reading. Massachusetts (1988).
2. R. C. Bolles, Robust feature matching through maximal cliques, Proc. Soc. Photo-Opt. Instrum. Engrs 182, 140-149 (1979).
3. K. L. Boyer and A. C. Kak, Structural stereopsis for 3-D vision, IEEE Trans. Pattern Anal. Mach. Intell., 10, 144-166 (1988).
4. L. S. Davis, Shape matching using relaxation techniques, IEEE Trans. Pattern Anal. Mach. Intell. 1(1), 60-72 (1979).
5. O. D. Faugeras and M. Hebert, The representation, recognition, and locating of 3-D objects, Int. J. Robotics Res. 5(3), 27-52 (1986).
6. M. Fischler and R. Elschlager, The representation and matching of pictorial structures, IEEE Trans. Comput. 22, 67-92 (1973).
7. D. E. Gharhaman, A. K. C. Wong and T. Au, Graph optimal monomorphism algorithms, IEEE Trans. Systems Man Cybernet., 10(4), 181-188 (1980).
8. W. E. L. Grimson and T. Lozano-Perez, Localizing overlapping parts by searching the interpretation tree, IEEE Trans. Pattern Analy. Mach. Intell. 9, 469-482 (1987).
9. B. Radig., Image sequence analysis using relational structures, Pattern Recognition. 17, 161-167 (1984).
10. L. G. Shapiro and R. M. Haralick, Structural description and inexact matching, IEEE Trans. Pattern Anal. Mach. Intell. 3, 504-519 (1981).
11. N. M. Vaidya and K. L. Boyer, Stereopsis and image registration from extended range features in the absence of camera pose information, Proc. Conf. on Computer Vision and Pattern Recognition, Vol. 76 (1982).
12. B. Yang, W. E. Snyder and G. L. Bilbro, Matching oversegmented 3-D images to models using association graphs, Image Vision Comput. 7, 135-143 (1989).
13. S. Z. Li, Matching: invariant to translations, rotations and scale changes, Pattern Recognition 25, 583-594 (1992).
14. D. H. Ackley, G. E. Hinton and T. J. Sejnowski, A learning algorithm for Boltzmann machines, Cognitive Sci., 9, 147-169 (1985).
15. S. Kirkpatrick, C. D. Gellatt and M. P. Vecchi, Optimisation by simulated annealing, Science 220, 671-680 (1983).
16. D. Geiger and F. Girosi. Parallel and deterministic algorithms from MRF's: surface reconstruction, IEEE Trans. Pattern Analy. Mach. Intell., 13(5), 401-412, (1991).
17. A. Blake, The least disturbance principle and weak constraints, Pattern Recognition Lett., 1, 393-399 (1983).
18. A. Blake and A. Zisserman, Visual Reconstruction, MIT Press, Cambridge, Massuchusetts (1987).
19. J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, Proc. National Academic Science, Vol. 81, pp. 3088-3092, U.S.A. (1984).
20. C. Koch, J. Marroquin and A. Yuille, Analog 'neuronal' networks in early vision, Proc. National Academic Science, Vol. 83, pp 4263-4267, U.S.A. (1986).
21. A. Witkin, D. Terzopoulos and M. Kass, Signal matching through scale space, Int. J. Comput. Vision 1, 133-144 (1987).
22. B. Bhanu, Representation and shape matching of 3-D objects, IEEE Trans. Pattern Analy. Mach. Intell. 6, 340-351 (1984).
23. B. Bhanu and O. D. Faugeras, Shape matching of two-dimensional objects, IEEE Trans. Pattern Analy. Mach. Intell., 6, 137-156 (1984).
24. O. D. Faugeras and M. Berthod, Improving consistency and reducing ambiguity in stochastic labeling: An optimization approach, IEEE Trans. Pattern Anal. Mach. Intell. 3, 412-423 (1981).
25. R. A. Hummel and S. W. Zucker, On the foundations of relaxation labeling processes, IEEE Trans. Pattern Analy. Mach. Intell. 5(3), 267-286 (1983).
26. A. Rosenfeld, R. Hummel and S. Zucker, Scene labeling by relaxation operations, IEEE Trans. Systems Man Cybernet. 6, 420-433 (1976).
27. S. Ullman, Relaxation and constraint optimization by local process. Comput. Graph. Image Process. 10, 115-195 (1979).
28. R. C. Read and D. G. Corneil, The graph isomorphism disease, J. Graph Theory 1, 339-363 (1977).
29. J. R. Ullman, An algorithm for subgraph isomorphism, J. Assoc. Comput. Machinery 23(1), 31-42 (1976).
30. F. Depiero, M. Trivedi and S. Serbin, Graph matching using a direct classification of node attendance, Pattern Recognition 29(6), 1031-1048 (1996).
31. S. Umeyama, An eigendecomposition approach to weighted graph matching problems, IEEE Trans. Pattern Anal. Mach. Intell. 10(5), 695-703 (1988).
32. L. G. Shapiro and R. M. Haralick, Structural descriptions and inexact matching, IEEE Trans. Pattern Anal. Mach. Intell., 3, 504-519 (1981).
33. H. Bunke and G. Allerman, Inexact graph matching for structural pattern recognition, Pattern Recognition Lett. 1, 4, 245-253 (1983).
34. A. Sanfeliu and K. S. Fu, A distance measure between attributed relational graphs for pattern recognition, IEEE Trans. Systems Man Cybernet. 13, 353-363 (1983).
35. R. Levinson, Pattern associativity and the retrieval of semantic networks, Comput. Math. Appl. 23, 573-600 (1992).
36. R. Horaud and T. Skordas, Stereo correspondence through feature grouping and maximal cliques, IEEE Trans. Pattern Anal. Machine Intell. 11(11), 1168-1180 (1989).
37. W. J. Christmas, J. Kittler and M. Petrou, Structural matching in computer vision using probabilistic relaxation. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 749-764 (1995).
38. J. Kittler, W. J. Christmas and M. Petrou, Probabilistic relaxation for matching problems in computer vision. Proc. 4th Int. Conf. on Computer Vision, pp. 666-673, Berlin, Germany (1993).
39. W. J. Christmas, J. Kittler and M. Petrou, Modelling compatibility coefficient distributions for probabilistic feature-labelling schemes, in Proc. 6th British Machine Vision Conference, D. Pycock, ed., Vol. 2, pp 603-612, Birmingham, U.K. (September 1995).
40. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, Wiley, New York (1987).
41. B. Gidas, A renormalization group approach to image processing problems, IEEE Trans. Pattern Anal. Mach. Intell. 11, 164-180 (1989).