The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 13, Issue 9, 1991, Pages 920-935

  Grimson, W Eric L ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Complexity hounds; constrained search; object recognition

Indexed keywords

COMPUTER PROGRAMMING - ALGORITHMS; MATHEMATICAL TECHNIQUES - COMBINATORIAL MATHEMATICS;

CLUTTERED ENVIRONMENTS; HEURISTIC SEARCH; OBJECT RECOGNITION; SEARCH TERMINATION;

PATTERN RECOGNITION;

EID: 0026221780     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.93810     Document Type: Article

References (31)

1. N. Ayache and O. D. Faugeras, “HYPER: A new approach for the recognition and positioning of two-dimensional objects,” IEEE Trans. Patt. Anal. Machine Intel!., vol. 8, no. 1, pp. 44–54, 1986.
2. D. H. Ballard, “Generalizing the Hough transform to detect arbitrary patterns,” Patt. Recogn., vol. 13, no. 2, 111–122, 1981.
3. R. C. Bolles and R.A. Cain, “Recognizing and locating partially visible objects: The local feature focus method,” Int. J. Robotics Res., vol. 1, no. 3, pp. 57–82, 1982.
4. M. Drumheller, “Mobile robot localization using sonar,” IEEE Trans. Patt. Anal. Machine Intell, vol. 9, no. 2, 325–332, 1987.
5. O. D. Faugeras and M. Hebert, “The representation, recognition, and locating of 3-D objects,” Int. J. Robotics Res., vol. 5, no. 3, pp. 27–52, 1986.
6. E. C. Freuder, “Synthesizing constraint expressions,” Comm. ACM, vol. 21, no. 11, pp. 958–966, 1978.
7. E. C. Freuder, “A sufficient condition for backtrack-free search,” J. ACM, vol. 29, no. 1, pp. 24–32, 1982.
8. J. Gaschnig, “Performance measurement and analysis of certain search algorithms,” Ph. D. thesis, Carnegie-Mellon Univ., Comput. Sci. Dept., Pittsburgh, PA, 1979.
9. P. C. Gaston and T. Lozano-Perez, “Tactile recognition and localization using object models: The case of polyhedra on a plane,” IEEE Trans. Patt. Anal. Machine Intell, vol. 6, no. 3, pp. 257–265, 1984.
10. W. E. L. Grimson, “The combinatorics of object recognition in cluttered environments using constrained search,” Artificial Intell, vol. 44, nos. 1–2, pp. 121–165, 1990.
11. W. E. L. Grimson, “The effect of indexing on the complexity of object recognition,” in Proc. Third Int. Conf. Comput. Vision (Osaka, Japan), 1990, pp. 644–651.
12. W. E. L. Grimson and D.P. Huttenlocher, “On the sensitivity of the Hough transform for object recognition,” in Proc. Second Int. Conf. Comput. Vision (Tarpon Springs, FL), 1988, pp. 700–706.
13. W. E. L. Grimson and D.P. Huttenlocher,.“On the verification of hypothesized matches in model-based recognition,” IEEE Trans. Patt. Anal. Machine Intell, to be published.
14. W. E. L. Grimson and T. Lozano-Perez, “Model-based recognition and localization from sparse range or tactile data,” Int. J. Robotics Res., vol. 3, no. 3, pp. 3–35, 1984.
15. W. E. L. Grimson and T. Lozano-Perez, “Localizing overlapping parts by searching the interpretation tree,” IEEE Trans. Patt. Anal. Machine Intell, vol. 9, no. 4, pp. 469-482, 1987.
16. R. M. Haralick and G.L. Elliot, “Increasing tree search efficiency for constraint satisfaction problems,” Artificial Intell., vol. 14, pp. 263–313, 1980.
17. R. M. Haralick and L.G. Shapiro, “The consistent labeling problem: Part 1,” IEEE Trans. Patt. Anal. Machine Intel!., vol. 1, no. 4, pp. 173–184, 1979.
18. D. P. Huttenlocher and S. Ullman, “Object recognition using alignment,” in Proc. First Int. Conf. Comput. Vision (London), 1987, pp. 102–111.
19. D. P. Huttenlocher, “Three-dimensional recognition of solid objects from a two-dimensional image,” M.I.T. Artificial Intell. Lab., TR 1045, 1989.
20. D. W. Jacobs, “The use of grouping in visual object recognition,” M.I.T. Artificial Intell. Lab., Memo 1023, 1988.
21. D. G. Lowe, Perceptual Organization and Visual Recognition. Boston: Kluwer Academic, 1985.
22. D. G. Lowe, “Three-dimensional object recognition from single two-dimensional images,” Artificial Intell, vol. 31, pp. 355–395, 1987.
23. A. K. Mackworth, “Consistency in networks of constraints,” Artificial Intell, vol. 8, pp. 99–118, 1977.
24. A. K. Mackworth and E.C. Freuder, “The complexity of some polynomial network consistency algorithms for constraint satisfaction problems,” Artificial Intell, vol. 25, pp. 65–74, 1985.
25. U. Montanari, “Networks of constraints: Fundamental properties and applications to picture processing,” Inform. Sci., vol. 7, pp. 95–132, 1974.
26. D. W. Murray, “Model-based recognition using 3D structure from motion,” Image Vision Comput., pp. 85–90, 1987.
27. D. W. Murray, “Model-based recognition using 3D shape alone,” Comput. Vision Graphics Image Processing, vol. 40, pp. 250–266, 1987.
28. D. W. Murray and D.B. Cook, “Using the orientation of fragmentary 3D edge segments for polyhedral object recognition,” Int. J. Comput. Vision, vol 2, no. 2, 153–169, 1988.
29. B. Nudel, “Consistent-labeling problems and their algorithms: Expected-complexities and theory-based heuristics,” Artificial Intell, vol. 21, pp. 135–178, 1983.
30. A. Sha’ashua and S. Ullman, “Structural saliency: The detection of globally salient structures using a locally connected network,” in Proc. Second Int. Conf. Comput. Vision (tarpon Springs, FL), 1988, pp. 321–327.
31. D. Waltz, “Understanding line drawings of scenes with shadows,” in The Psychology of Computer Vision (P. Winston, Ed.). New York: McGraw Hill, 1975, pp. 19–91.