Polynomial-Time Geometric Matching for Object Recognition

International Journal of Computer Vision

Volumn 21, Issue 1-2, 1997, Pages 37-61

  Cass, Todd A ^a  

^a PALO ALTO RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL GEOMETRY; COMPUTER SIMULATION; COMPUTER VISION; CONSTRAINT THEORY; FEATURE EXTRACTION; POLYNOMIALS;

GEOMETRIC CONSTRAINTS; GEOMETRIC FEATURE MATCHING METHOD;

OBJECT RECOGNITION;

EID: 0030699687     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1007971405872     Document Type: Article

References (30)

1. Alt, H., Mehlhorn, K., Wagener, H., and Welzl, E. 1988. Congruence, similarity, and symmetries of geometric objects. In Discrete and Computational Geometry, Springer-Verlag: New York, vol. 3, pp. 237-256.
2. Alter, D.T. 1992. Robust and efficient 3d recognition by alignment. Master's Thesis, Massachusetts Institute of Technology.
3. Arkin, E., Kedem, K., Mitchell, J., Sprinzak, J., and Werman, M. 1991. Matching points into noise regions: Combinatorial bounds and algorithms. In Proceedings of the Second ACM-SIAM Symposium on Discrete Algorithms.
4. Ayache, N. and Faugeras, O.D. 1986. Hpyer: A new approach for the recognition and positioning of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):44-54.
5. Baird, H.S. 1985. Model-Based Image Matching Using Location. MIT Press: Cambridge, MA.
6. Ballard, D.H. 1981. Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition, 13:111-122.
7. Breuel, T.M. 1990. An efficient correspondence based algorithm for 2d and 3d model based recognition. A.I. Memo No. 1259, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
8. Cass, T.A. 1988. Parallel computation in model-based recognition. Master's Thesis, Massachusetts Institute of Technology.
9. Cass, T.A. 1990. Feature matching for object localization in the presence of uncertainty. In Proceedings of the International Conference on Computer Vision, Osaka, Japan.
10. Cass, T.A. 1992. Polynomial-Time Geometric Matching for Object Recognition. Ph.D. Thesis, Massachusetts Institute of Technology.
11. Cass, T.A. 1996. Robust affine structure matching for 3d object recognition. In Proceedings of the European Conference on Computer Vision, Cambridge: England.
12. Chew, L., Goodrich, M., Huttenlocher, D., Kedem, K., Kleinberg, J., and Kravets, D. 1993. Geometric pattern matching under euclidean motion. In Proceedings of the Fifth Canadian Conference on Computational Geometry, Waterloo, Ontario, Canada.
13. Clemens, D.T. 1986. The recognition of two-dimensional modeled objects in images. Master's Thesis, Massachusetts Institute of Technology.
14. Costa, M., Haralick, R.M., and Shapiro, L.G. 1990. Optimal affine- invariant point matching. In Proceedings of the 6th Israel Conference on AI, pp. 35-61.
15. Edelsbrunner, H. 1987. Algorithms in Combinatorial Geometry. Springer-Verlag.
16. Goodrich, M., Mitchell, J., and Orletsky, M. 1994. Practical methods for approximate geometric pattern matching under rigid motion. In Proceedings of the 10th ACM Sympos. Computational Geometry, pp. 103-112.
17. Grimson, W.E.L. 1990. The combinatorics of object recognition in cluttered environments using constrained search. Artificial Intelligence, 44:121-166.
18. Grimson, W.E.L. and Lozano-Perez, T. 1987. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4):469-482.
19. Heffernan, P. and Schirra, S. 1994. Approximate decision algorithms for point set congruence. Computational Geometry: Theory and Applications, 4.
20. Huttenlocher, D. and Ullman, S. 1990. Recognizing solid objects by alignment with an image. International Journal of Computer Vision, 5(7):195-212.
21. Huttenlocher, D.P. and Cass, T.A. 1992. Measuring the quality of hypotheses in model-based recognition. In Proceedings of the European Conference on Computer Vision, Geneva, Italy.
22. Jacobs, D.W. 1992. Recognizing 3-D Objects Using 2-D Images. Ph.D. Thesis, Massachusetts Institute of Technology.
23. Lamdan, Y. and Wolfson, H. 1988. Geometric hashing: A general and efficient model-based recognition scheme. In Proceedings of the International Conference on Computer Vision, pp. 238-249.
24. Papadimitriou, C.H. and Steiglitz, K. 1982. Cominatorial Optimization: Algorithms and Complexity. Prentice-Hall: Englewood Cliffs, New Jersey.
25. Preparata, E.P. and Shamos, M. 1985. Computational Geometry - An Introduction. Springer-Verlag: New York.
26. Rigoutsos, I. and Hummel R. 1995. A bayesian approach to model matching with geometric hashing. International Journal of Computer Vision, 62(1):11-26.
27. Stockman, G., Kopstein, S., and Bennet, S. 1982. Matching images to models for registration and object detection vis clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(3).
28. Thompson, D. and Mundy, J. 1987. Three-dimensional model matching from an unconstrained viewpoint. In Proceedings of IEEE Conference on Robotics and Automation, pp. 208-220.
29. Ullman, S. and Basri, R. 1991. Recognition by linear combinations of models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(10):992-1007.
30. Wells III, W.M. 1991. Map model matching. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, Lahaina, Maui, HI.