On the use of interval arithmetic in geometric branch and bound algorithms

Pattern Recognition Letters

Volumn 24, Issue 9-10, 2003, Pages 1375-1384

  Breuel, T M ^a  

^a PALO ALTO RESEARCH CENTER   (United States)

Author keywords

Geometric matching; Interval arithmetic; Visual object recognition

Indexed keywords

ALGORITHMS; DIGITAL ARITHMETIC; ERROR DETECTION; GEOMETRY; PROBLEM SOLVING;

BOUND TECHNIQUES;

PATTERN RECOGNITION;

PATTERN RECOGNITION;

EID: 0037410683     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(02)00378-1     Document Type: Article

References (17)

1. Baird, H.S. 1985. Model-Based Image Matching Using Location. MIT Press, Cambridge, MA.
2. Breuel, T.M., 1992. Fast Recognition using Adaptive Subdivisions of Transformation Space. In: IEEE Conf. on Computer Vision and Pattern Recognition. pp. 445-451.
3. Breuel, T.M. 1996. Finding Lines under Bounded Error. Pattern Recognit., 29 (1), 167-178.
4. Breuel, T.M., 2002a. A comparison of search strategies for geometric branch and bound algorithms. In: Computer Vision - ECCV 2002, 7th European Conf. on Computer Vision. Proceedings, Part III, pp. 837-852.
5. Breuel, T.M., 2002b. Robust least square baseline finding using a branch and bound algorithm. In: Document Recognition and Retrieval VIII, SPIE, San Jose. pp. 20-27.
6. Grimson, E. 1990. Object Recognition by Computer. MIT Press, Cambridge, MA.
7. Hagedoorn, M., Veltkamp, R.C., 1997. Reliable and efficient pattern matching using an affine invariant metric. Technical Report RUU-CS-97-33, Department of Computing Science, Utrecht University.
8. Hansen, E. 1980. Global optimization using interval analysis-the multi-dimensional case. Numerische Mathematik, 34, 247-270.
9. Hansen, E., Greenberg, R.I., 1983. An interval newton method. Appl. Math. Comput., 12, 89-98.
10. Hickey, T.J., Ju, Q., van Emden, M.H., 2001. Interval arithmetic: from principles to implementation. J. ACM 48 (5), 1038-1068.
11. Huttenlocher, D., Klanderman, G., Rucklidge, W., 1993. Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Machine Intell. 15 (9), 850-863.
12. Huttenlocher, D.P., Ullman, S., June 1987. Object Recognition Using Alignment. In: Internat. Conf. on Computer Vision. IEEE, Washington, DC, London, England, pp. 102-111.
13. Jaulin, L., Kieffer, M., Didrit, O., Walter, E., 2001. Applied Interval Analysis. Springer Verlag, Berlin.
14. Jurie, F. 1999. Solution of the simultaneous pose and correspondence problem using gaussian error model. Computer Vision and Image Understanding, 73 (3), 357-373.
15. Mount, D., Netanyahu, N., Le Moigne, J., 1999. Efficient algorithms for robust feature matching. Pattern Recognit. 32 (1), 17-38.
16. Olson, C.F. 2001. Locating geometric primitives by pruning the parameter space. Pattern Recognit. 34 (6), 1247-1256.
17. Wells, W. 1997. Statistical approaches to feature-based object recognition. Internat. J. Comput. Vision 21 (1/2), 63-98.