Efficiently Locating Objects Using the Hausdorff Distance

International Journal of Computer Vision

Volumn 24, Issue 3, 1997, Pages 251-270

  Rucklidge, William J ^a  

^a PALO ALTO RESEARCH CENTER   (United States)

Author keywords

Affine transformation; Distance transform; Efficient search; Feature based matching; Guaranteed search; Hausdorff distance; Model based recognition; Object recognition

Indexed keywords

FEATURE EXTRACTION; IMAGE ANALYSIS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; SET THEORY;

AFFINE TRANSFORMATIONS; HAUSDORFF DISTANCE;

OBJECT RECOGNITION;

EID: 0031237788     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007975324482     Document Type: Article

References (15)

1. Ayache, N. and Faugeras, O. 1986. HYPER: A new approach for the recognition and positioning of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):44-54.
2. Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., and Wolf, H.C. 1977. Parametric correspondence and chamfer matching: Two new techniques for image matching. In Proc. Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA, pp. 659-663.
3. Borgefors, G. 1988. Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6):849-865.
4. Breu, H., Gil, J., Kirkpatrick, D., and Werman, M. 1995. Linear time Euclidean distance transform algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(5):529-533.
5. Bruel, T.M. 1992. Fast recognition using adaptive subdivision of transformation space. In Proc. Computer Vision and Pattern Recognition, Champaign-Urbana, Illinois, pp. 445-451.
6. Cass, T.A. 1990. Feature matching for object localization in the presence of uncertainty. In Proc. Third International Conference on Computer Vision, Osaka, Japan, pp. 360-364.
7. Danielsson, P.E. 1980. Euclidean distance mapping. Computer Graphics and Image Processing, 14:227-248.
8. Huttenlocher, D.P. and Ullman, S. 1990. Recognizing solid objects by alignment with an image. International Journal of Computer Vision, 5(2):195-212.
9. Huttenlocher, D.P. and Rucklidge, W.J. 1993. A multi-resolution technique for comparing images using the Hausdorff distance. In Proc. Computer Vision and Pattern Recognition, New York, NY, pp. 705-706.
10. Huttenlocher, D.P., Klanderman, G.A., and Rucklidge, W.J. 1993. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850-863.
11. Olson, C.F. 1994. Time and space efficient pose clustering. In Proc. Computer Vision and Pattern Recognition, Seattle, Washington, pp. 251-258.
12. Paglieroni, D.W. 1992. Distance transforms: Properties and machine vision applications. Computer Vision, Graphics and Image Proc.: Graphical Models and Image Processing, 54(1):56-74.
13. Paglieroni, D.W., Ford, G.E., and Tsujimoto, E.M. 1994. The position-orientation masking approach to parametric search for template matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(7):740-747.
14. Rucklidge, W.J. 1995a. Efficient Computation of the Minimum Hausdorff Distance for Visual Recognition. Ph.D. thesis, Cornell University.
15. Rucklidge, W.J. 1995b. Locating objects using the Hausdorff distance. In Proc. Fifth International Conference on Computer Vision, Cambridge, MA, pp. 457-464.