On the Verification of Hypothesized Matches in Model-Based Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 13, Issue 12, 1991, Pages 1201-1213

  Grimson, W Eric L ^a   Huttenlocher, Daniel P ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Department of Obstetrics Gynecology   (United States)

Author keywords

Hypothesis verification; hypothesize and test; object recognition; occupancy models

Indexed keywords

PROBABILITY--RANDOM PROCESSES; STATISTICAL METHODS;

MODEL-BASED RECOGNITION; RANDOM MATCHES; STATISTICAL OCCUPANCY PROBLEM;

PATTERN RECOGNITION;

EID: 0026402027     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.106994     Document Type: Article

References (19)

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 Intell., vol. PAMI-8, no. 1, pp. 44—54, 1986.
2. P. J. Besl and R. C. Jain, “Three-dimensional object recognition,” ACM Comput. Surveys, vol. 17, no. 1, pp. 75–154, 1985.
3. R. T. Chin and C. R. Dyer, “Model-based recognition in robot vision,” ACM Comput. Surveys vol. 18, no. 1, pp. 67–108, 1986.
4. D. T. Clemens, “The recognition of two-dimentional modeled objects-in images,” M. Sc. thesis, Mass. Inst. Technol., Dept. Elec. Eng. Comput. Sci., 1986.
5. G. J. Ettinger, “Large hierarchical object recognition using libraries of parameterized model sub-parts,” Patt. Recog., pp. 32-41 1988.
6. 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.
7. W. Feller, An Introduction to Probability Theory and Its Applications. New York: Wiley, 1986.
8. M. A. Fischler and R. C. Bolles, “Random sample concensus: A paradigm for model fitting applications to image analysis and automated cartography,” Commun. ACM vol. 24, pp. 381–395, 1981.
9. W. E. L. Grimson, “On the recognition of parameterized 2D objects,” Int. J. Comput. Vision, vol. 2, no. 4, pp. 353–372, 1989.
10. W. E. L. Grimson, “The combinatorics of object recognition in cluttered environments using constrained search,” Artificial Intell, vol. 44, pp. 121–165, 1990.
11. W. E. L. Grimson and D. P. Huttenlocher, “On the sensitivity of the Hough transform for object recognition,” IEEE Trans. Patt. Anal. Machine Intelligence, vol. 12, no. 3, pp. 255–274, 1990.
12. W. E. L. Grimson and D. P. Huttenlocher, “On the verification of hypothesized matches in model-based recognition,” MIT AI Lab Memo 1110, 1989.
13. 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.
14. D. P. Huttenlocher and S. Ullman, “Recognizing solid objects by alignment with an image,” J. Comput. Vision, vol. 5, no. 2, pp. 195–212, 1990.
15. K. Ikeuchi, “Generating an interpretation tree from a CAD model for 3-D object recognition in bin-picking tasks,” Int. J. Comput. Vision, vol. 1, no. 2, pp. 145–165, 1987.
16. A. M. Mood, F. A. Graybill, and D. C. Boes, Introduction to the Theory of Statistics. New York, McGraw-Hill, 1974
17. 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, pp. 153–169, 1988.
18. G. Stockman, “Object recognition and localization via pose clustering,” Comp. Vision Graphics Image Processing, vol. 40, pp. 361–387, 1987.
19. D. W. Thompson and J. L. Mundy, “Three-dimensional model matching from an unconstrained viewpoint,” inProc. Int. Conf. Robotics Automat. (Raleigh, NC), 1987, pp. 208–220.