Abstract

We propose a geometric matching technique in which line segments and elliptical arcs are used as edge features. The use of these higher-order features renders feature representation efficient. We derive distance measures to evaluate the similarity between the features of the model and those of the image. The model transformation parameters are found by searching a 3-D transformation space using cell-decomposition. The performance of the proposed method is quite good when tested on a variety of images.

© 2010 Optical Society of America

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References

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  1. W. J. Rucklidge, Efficient Visual Recognition Using the Hausdorff Distance (Lecture Notes in Computer Science, Springer-Verlag, 1996).
    [CrossRef]
  2. B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21, 977-1000 (2003).
    [CrossRef]
  3. M. T. Goodrich, J. S. B. Mitchell, and M. W. Orletsky, “Approximate geometric pattern matching under rigid motions,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 371-379 (1999).
    [CrossRef]
  4. T. M. Breuel, “Geometric aspects of visual object recognition,” Ph.D. dissertation (Massachusetts Institute of Technology, 1992).
  5. J. R. Beveridge and E. M. Riseman, “How easy is matching 2D line models using local search?” IEEE Trans. Pattern Anal. Mach. Intell. 19, 564-579 (1997).
    [CrossRef]
  6. W. E. L. Grimson, “On the recognition of curved objects,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 632-643 (1989).
    [CrossRef]
  7. H. J. Wolfson, “On curve matching,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 483-489 (1990).
    [CrossRef]
  8. P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
    [CrossRef]
  9. H. S. Alhichri and M. Kamel, “Virtual circles: a new set of features for fast image registration,” Pattern Recogn. Lett. 24, 1181-1190 (2003).
    [CrossRef]
  10. N. Ayache and O. D. Faugeras, “HYPER: a new approach for the recognition and positioning of two-dimensional objects,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 44-54 (1986).
    [CrossRef] [PubMed]
  11. J. L. Mundy and A. J. Heller, “The evolution and testing of a model-based object recognition system,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 1990) p. 268.
  12. D. Huttenlocher, D. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (1995).
    [CrossRef]
  13. M. Hagedoorn and R. C. Veltkamp, “Reliable and efficient pattern matching using an affine invariant metric,” Int. J. Comput. Vis. 31, 203-225 (1999).
    [CrossRef]
  14. G. Stockman, “Object recognition and localization via pose clustering,” Comput. Vis. Graph. Image Process. 40, 361-387 (1987).
    [CrossRef]
  15. X. Yi and O. I. Camps, “Line-based recognition using a multidimensional Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 901-916 (1999).
    [CrossRef]
  16. T. M. Breuel, “Implementation techniques for geometric branch-and-bound matching methods,” Comput. Vis. Image Underst. 90, 258-294 (2003).
    [CrossRef]
  17. C. Guerraa and V. Pascucci, “Line-based object recognition using Hausdorff distance: from range images to molecular secondary structures,” Image Vis. Comput. 23, 405-415 (2005).
    [CrossRef]
  18. L. K. Shark, A. A. Kurekin, and B. J. Matuszewski, “Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments,” Pattern Recogn. 40, 1432-1450 (2007).
    [CrossRef]
  19. R. Yang and Y. Gao, “Line-based affine invariant object location using transformation space decomposition,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 646-649.
  20. T. M. Breuel, “On the use of interval arithmetic in geometric branch and bound algorithms,” Pattern Recogn. Lett. 24, 1375-1384 (2003).
    [CrossRef]
  21. W. Wan and J. A. Ventura, “Segmentation of planar curves into straight-line segments and elliptical arcs,” Graph. Models Image Process. 59, 484-494 (1997).
    [CrossRef]
  22. T. M. Breuel, “Fast recognition using adaptive subdivisions of transformation space,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1992), p. 445.
  23. C. Paramanand and A. N. Rajagopalan, “An efficient representation of digital curves with line segments and elliptical arcs,” in Proceedings of IET International Conference on Visual Information Engineering (IET, 2006), pp. 470-475.
  24. C. Paramanand and A. N. Rajagopalan, “Efficient geometric matching with higher-order features,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2008) pp. 1-4.
  25. T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing (Prentice Hall, 2000).
  26. A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 476-480 (1999).
    [CrossRef]
  27. Z. Zhang, “Parameter estimation techniques: A tutorial with application to conic fitting,” Image Vis. Comput. 15, 59-76 (1997).
    [CrossRef]
  28. R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).
  29. D. Scharstein and C. Pal, “Learning conditional random fields for stereo,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), p. 1.

2007 (1)

L. K. Shark, A. A. Kurekin, and B. J. Matuszewski, “Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments,” Pattern Recogn. 40, 1432-1450 (2007).
[CrossRef]

2006 (1)

R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).

2005 (2)

C. Guerraa and V. Pascucci, “Line-based object recognition using Hausdorff distance: from range images to molecular secondary structures,” Image Vis. Comput. 23, 405-415 (2005).
[CrossRef]

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[CrossRef]

2003 (4)

H. S. Alhichri and M. Kamel, “Virtual circles: a new set of features for fast image registration,” Pattern Recogn. Lett. 24, 1181-1190 (2003).
[CrossRef]

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21, 977-1000 (2003).
[CrossRef]

T. M. Breuel, “On the use of interval arithmetic in geometric branch and bound algorithms,” Pattern Recogn. Lett. 24, 1375-1384 (2003).
[CrossRef]

T. M. Breuel, “Implementation techniques for geometric branch-and-bound matching methods,” Comput. Vis. Image Underst. 90, 258-294 (2003).
[CrossRef]

1999 (4)

X. Yi and O. I. Camps, “Line-based recognition using a multidimensional Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 901-916 (1999).
[CrossRef]

M. Hagedoorn and R. C. Veltkamp, “Reliable and efficient pattern matching using an affine invariant metric,” Int. J. Comput. Vis. 31, 203-225 (1999).
[CrossRef]

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 476-480 (1999).
[CrossRef]

M. T. Goodrich, J. S. B. Mitchell, and M. W. Orletsky, “Approximate geometric pattern matching under rigid motions,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 371-379 (1999).
[CrossRef]

1997 (3)

J. R. Beveridge and E. M. Riseman, “How easy is matching 2D line models using local search?” IEEE Trans. Pattern Anal. Mach. Intell. 19, 564-579 (1997).
[CrossRef]

Z. Zhang, “Parameter estimation techniques: A tutorial with application to conic fitting,” Image Vis. Comput. 15, 59-76 (1997).
[CrossRef]

W. Wan and J. A. Ventura, “Segmentation of planar curves into straight-line segments and elliptical arcs,” Graph. Models Image Process. 59, 484-494 (1997).
[CrossRef]

1995 (1)

D. Huttenlocher, D. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (1995).
[CrossRef]

1990 (1)

H. J. Wolfson, “On curve matching,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 483-489 (1990).
[CrossRef]

1989 (1)

W. E. L. Grimson, “On the recognition of curved objects,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 632-643 (1989).
[CrossRef]

1987 (1)

G. Stockman, “Object recognition and localization via pose clustering,” Comput. Vis. Graph. Image Process. 40, 361-387 (1987).
[CrossRef]

1986 (1)

N. Ayache and O. D. Faugeras, “HYPER: a new approach for the recognition and positioning of two-dimensional objects,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 44-54 (1986).
[CrossRef] [PubMed]

Alhichri, H. S.

H. S. Alhichri and M. Kamel, “Virtual circles: a new set of features for fast image registration,” Pattern Recogn. Lett. 24, 1181-1190 (2003).
[CrossRef]

Ayache, N.

N. Ayache and O. D. Faugeras, “HYPER: a new approach for the recognition and positioning of two-dimensional objects,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 44-54 (1986).
[CrossRef] [PubMed]

Beveridge, J. R.

J. R. Beveridge and E. M. Riseman, “How easy is matching 2D line models using local search?” IEEE Trans. Pattern Anal. Mach. Intell. 19, 564-579 (1997).
[CrossRef]

Breuel, T. M.

T. M. Breuel, “Implementation techniques for geometric branch-and-bound matching methods,” Comput. Vis. Image Underst. 90, 258-294 (2003).
[CrossRef]

T. M. Breuel, “On the use of interval arithmetic in geometric branch and bound algorithms,” Pattern Recogn. Lett. 24, 1375-1384 (2003).
[CrossRef]

T. M. Breuel, “Fast recognition using adaptive subdivisions of transformation space,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1992), p. 445.

T. M. Breuel, “Geometric aspects of visual object recognition,” Ph.D. dissertation (Massachusetts Institute of Technology, 1992).

Camps, O. I.

X. Yi and O. I. Camps, “Line-based recognition using a multidimensional Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 901-916 (1999).
[CrossRef]

Faugeras, O. D.

N. Ayache and O. D. Faugeras, “HYPER: a new approach for the recognition and positioning of two-dimensional objects,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 44-54 (1986).
[CrossRef] [PubMed]

Felzenszwalb, P. F.

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[CrossRef]

Fisher, R. B.

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 476-480 (1999).
[CrossRef]

Fitzgibbon, A. W.

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 476-480 (1999).
[CrossRef]

Fitzsimons, M. E.

R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).

Flusser, J.

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21, 977-1000 (2003).
[CrossRef]

Gao, Y.

R. Yang and Y. Gao, “Line-based affine invariant object location using transformation space decomposition,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 646-649.

Goodrich, M. T.

M. T. Goodrich, J. S. B. Mitchell, and M. W. Orletsky, “Approximate geometric pattern matching under rigid motions,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 371-379 (1999).
[CrossRef]

Grimson, W. E. L.

W. E. L. Grimson, “On the recognition of curved objects,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 632-643 (1989).
[CrossRef]

Guerraa, C.

C. Guerraa and V. Pascucci, “Line-based object recognition using Hausdorff distance: from range images to molecular secondary structures,” Image Vis. Comput. 23, 405-415 (2005).
[CrossRef]

Hagedoorn, M.

M. Hagedoorn and R. C. Veltkamp, “Reliable and efficient pattern matching using an affine invariant metric,” Int. J. Comput. Vis. 31, 203-225 (1999).
[CrossRef]

Haider, A.

R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).

Heller, A. J.

J. L. Mundy and A. J. Heller, “The evolution and testing of a model-based object recognition system,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 1990) p. 268.

Huttenlocher, D.

D. Huttenlocher, D. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (1995).
[CrossRef]

Huttenlocher, D. P.

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[CrossRef]

Kamel, M.

H. S. Alhichri and M. Kamel, “Virtual circles: a new set of features for fast image registration,” Pattern Recogn. Lett. 24, 1181-1190 (2003).
[CrossRef]

Klanderman, D.

D. Huttenlocher, D. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (1995).
[CrossRef]

Kurekin, A. A.

L. K. Shark, A. A. Kurekin, and B. J. Matuszewski, “Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments,” Pattern Recogn. 40, 1432-1450 (2007).
[CrossRef]

Matuszewski, B. J.

L. K. Shark, A. A. Kurekin, and B. J. Matuszewski, “Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments,” Pattern Recogn. 40, 1432-1450 (2007).
[CrossRef]

Millane, R. P.

R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).

Mitchell, J. S. B.

M. T. Goodrich, J. S. B. Mitchell, and M. W. Orletsky, “Approximate geometric pattern matching under rigid motions,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 371-379 (1999).
[CrossRef]

Moon, T. K.

T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing (Prentice Hall, 2000).

Mundy, J. L.

J. L. Mundy and A. J. Heller, “The evolution and testing of a model-based object recognition system,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 1990) p. 268.

Orletsky, M. W.

M. T. Goodrich, J. S. B. Mitchell, and M. W. Orletsky, “Approximate geometric pattern matching under rigid motions,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 371-379 (1999).
[CrossRef]

Pal, C.

D. Scharstein and C. Pal, “Learning conditional random fields for stereo,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), p. 1.

Paramanand, C.

C. Paramanand and A. N. Rajagopalan, “An efficient representation of digital curves with line segments and elliptical arcs,” in Proceedings of IET International Conference on Visual Information Engineering (IET, 2006), pp. 470-475.

C. Paramanand and A. N. Rajagopalan, “Efficient geometric matching with higher-order features,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2008) pp. 1-4.

Pascucci, V.

C. Guerraa and V. Pascucci, “Line-based object recognition using Hausdorff distance: from range images to molecular secondary structures,” Image Vis. Comput. 23, 405-415 (2005).
[CrossRef]

Pilu, M.

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 476-480 (1999).
[CrossRef]

Qi, M.

R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).

Rajagopalan, A. N.

C. Paramanand and A. N. Rajagopalan, “Efficient geometric matching with higher-order features,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2008) pp. 1-4.

C. Paramanand and A. N. Rajagopalan, “An efficient representation of digital curves with line segments and elliptical arcs,” in Proceedings of IET International Conference on Visual Information Engineering (IET, 2006), pp. 470-475.

Riseman, E. M.

J. R. Beveridge and E. M. Riseman, “How easy is matching 2D line models using local search?” IEEE Trans. Pattern Anal. Mach. Intell. 19, 564-579 (1997).
[CrossRef]

Rucklidge, W. J.

D. Huttenlocher, D. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (1995).
[CrossRef]

W. J. Rucklidge, Efficient Visual Recognition Using the Hausdorff Distance (Lecture Notes in Computer Science, Springer-Verlag, 1996).
[CrossRef]

Scharstein, D.

D. Scharstein and C. Pal, “Learning conditional random fields for stereo,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), p. 1.

Shark, L. K.

L. K. Shark, A. A. Kurekin, and B. J. Matuszewski, “Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments,” Pattern Recogn. 40, 1432-1450 (2007).
[CrossRef]

Stirling, W. C.

T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing (Prentice Hall, 2000).

Stockman, G.

G. Stockman, “Object recognition and localization via pose clustering,” Comput. Vis. Graph. Image Process. 40, 361-387 (1987).
[CrossRef]

Veltkamp, R. C.

M. Hagedoorn and R. C. Veltkamp, “Reliable and efficient pattern matching using an affine invariant metric,” Int. J. Comput. Vis. 31, 203-225 (1999).
[CrossRef]

Ventura, J. A.

W. Wan and J. A. Ventura, “Segmentation of planar curves into straight-line segments and elliptical arcs,” Graph. Models Image Process. 59, 484-494 (1997).
[CrossRef]

Wan, W.

W. Wan and J. A. Ventura, “Segmentation of planar curves into straight-line segments and elliptical arcs,” Graph. Models Image Process. 59, 484-494 (1997).
[CrossRef]

Wolfson, H. J.

H. J. Wolfson, “On curve matching,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 483-489 (1990).
[CrossRef]

Yang, R.

R. Yang and Y. Gao, “Line-based affine invariant object location using transformation space decomposition,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 646-649.

Yi, X.

X. Yi and O. I. Camps, “Line-based recognition using a multidimensional Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 901-916 (1999).
[CrossRef]

Zhang, Z.

Z. Zhang, “Parameter estimation techniques: A tutorial with application to conic fitting,” Image Vis. Comput. 15, 59-76 (1997).
[CrossRef]

Zitova, B.

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21, 977-1000 (2003).
[CrossRef]

Comput. Vis. Graph. Image Process. (1)

G. Stockman, “Object recognition and localization via pose clustering,” Comput. Vis. Graph. Image Process. 40, 361-387 (1987).
[CrossRef]

Comput. Vis. Image Underst. (1)

T. M. Breuel, “Implementation techniques for geometric branch-and-bound matching methods,” Comput. Vis. Image Underst. 90, 258-294 (2003).
[CrossRef]

Graph. Models Image Process. (1)

W. Wan and J. A. Ventura, “Segmentation of planar curves into straight-line segments and elliptical arcs,” Graph. Models Image Process. 59, 484-494 (1997).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (8)

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 476-480 (1999).
[CrossRef]

X. Yi and O. I. Camps, “Line-based recognition using a multidimensional Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 901-916 (1999).
[CrossRef]

N. Ayache and O. D. Faugeras, “HYPER: a new approach for the recognition and positioning of two-dimensional objects,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 44-54 (1986).
[CrossRef] [PubMed]

D. Huttenlocher, D. Klanderman, and W. J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 850-863 (1995).
[CrossRef]

M. T. Goodrich, J. S. B. Mitchell, and M. W. Orletsky, “Approximate geometric pattern matching under rigid motions,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 371-379 (1999).
[CrossRef]

J. R. Beveridge and E. M. Riseman, “How easy is matching 2D line models using local search?” IEEE Trans. Pattern Anal. Mach. Intell. 19, 564-579 (1997).
[CrossRef]

W. E. L. Grimson, “On the recognition of curved objects,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 632-643 (1989).
[CrossRef]

H. J. Wolfson, “On curve matching,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 483-489 (1990).
[CrossRef]

Image Vis. Comput. (3)

B. Zitova and J. Flusser, “Image registration methods: a survey,” Image Vis. Comput. 21, 977-1000 (2003).
[CrossRef]

C. Guerraa and V. Pascucci, “Line-based object recognition using Hausdorff distance: from range images to molecular secondary structures,” Image Vis. Comput. 23, 405-415 (2005).
[CrossRef]

Z. Zhang, “Parameter estimation techniques: A tutorial with application to conic fitting,” Image Vis. Comput. 15, 59-76 (1997).
[CrossRef]

Int. J. Comput. Vis. (2)

M. Hagedoorn and R. C. Veltkamp, “Reliable and efficient pattern matching using an affine invariant metric,” Int. J. Comput. Vis. 31, 203-225 (1999).
[CrossRef]

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[CrossRef]

Pattern Recogn. (1)

L. K. Shark, A. A. Kurekin, and B. J. Matuszewski, “Development and evaluation of fast branch-and-bound algorithm for feature matching based on line segments,” Pattern Recogn. 40, 1432-1450 (2007).
[CrossRef]

Pattern Recogn. Lett. (2)

H. S. Alhichri and M. Kamel, “Virtual circles: a new set of features for fast image registration,” Pattern Recogn. Lett. 24, 1181-1190 (2003).
[CrossRef]

T. M. Breuel, “On the use of interval arithmetic in geometric branch and bound algorithms,” Pattern Recogn. Lett. 24, 1375-1384 (2003).
[CrossRef]

Proc. SPIE (1)

R. P. Millane, M. E. Fitzsimons, M. Qi, and A. Haider, “Analysis of gravel river beds using three-dimensional laser scanning,” Proc. SPIE 63160, 63160B.1-63160B.10 (2006).

Other (9)

D. Scharstein and C. Pal, “Learning conditional random fields for stereo,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), p. 1.

T. M. Breuel, “Fast recognition using adaptive subdivisions of transformation space,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1992), p. 445.

C. Paramanand and A. N. Rajagopalan, “An efficient representation of digital curves with line segments and elliptical arcs,” in Proceedings of IET International Conference on Visual Information Engineering (IET, 2006), pp. 470-475.

C. Paramanand and A. N. Rajagopalan, “Efficient geometric matching with higher-order features,” in Proceedings of IEEE Conference on Pattern Recognition (IEEE, 2008) pp. 1-4.

T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing (Prentice Hall, 2000).

W. J. Rucklidge, Efficient Visual Recognition Using the Hausdorff Distance (Lecture Notes in Computer Science, Springer-Verlag, 1996).
[CrossRef]

T. M. Breuel, “Geometric aspects of visual object recognition,” Ph.D. dissertation (Massachusetts Institute of Technology, 1992).

R. Yang and Y. Gao, “Line-based affine invariant object location using transformation space decomposition,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 646-649.

J. L. Mundy and A. J. Heller, “The evolution and testing of a model-based object recognition system,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 1990) p. 268.

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Figures (9)

Fig. 1
Fig. 1

(a),( b) Images of a computer mouse. (c), ( d). Feature representation of Figs. 1a, 1b, respectively.

Fig. 2
Fig. 2

Distance between model and image elliptical arcs.

Fig. 3
Fig. 3

(a) Model ellipse. (b) Target image containing a distorted instance of the model. (c) Feature representation of Fig. 3b. (d) Model located in the target image.

Fig. 4
Fig. 4

(a) Model image and its features. (b) Given image. (c) Image features. (d) Model correctly located in the image.

Fig. 5
Fig. 5

(a) Tube model. (b) Given image. (c) Image features. (d) Output result.

Fig. 6
Fig. 6

(a) Model racket and its features. (b) Given image. (c) Image features. (d) Racket located in the target image.

Fig. 7
Fig. 7

(a) Performance as a function of τ n . (b) Speed–accuracy trade-off.

Fig. 8
Fig. 8

(a) Model and its features. (b) Probe image. (c) Image features. (d) Model correctly located in the image.

Fig. 9
Fig. 9

Performance comparison over 1550 images. (a) Number of cells evaluated. (b) CPU run-time per group for the proposed and the point-matching methods.

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

d l ( m l , i l ) = { d s ( m l , i l ) , v 3 τ l d s ( m l , i l ) + v 3 2 , v 3 > τ l } ,
e j = ( x j cos α m + y j sin α m h r ) 2 a m 2 + ( x j sin α m + y j cos α m k r ) 2 b m 2 1 ,
e ( θ ) = ( x ( θ ) h ) 2 a m 2 + ( y ( θ ) k ) 2 b m 2 1 ,
d e ( m e , i e ) = θ 1 θ 2 e 2 ( θ ) d θ .
e j l = ( u x j + g sin α m h r ) 2 a m 2 + ( v x j + g cos α m k r ) 2 b m 2 1 ,
d e l = x = x 1 x = x 2 { ( u x + g sin α m h r ) 2 a m 2 + ( v x + g cos α m k r ) 2 b m 2 1 } 2 d x .
D ( t ( M ) , I ) = max { max m l M L [ min i l I L { d l ( t ( m l ) , i l ) } ] , max m e M E [ min i e , i l I { d e ( t ( m e ) , i e ) , d e l ( t ( m e ) , i l ) } ] } .
N f i N m > τ n .
Δ ( m ) = min i I d ( m , i ) ,
D mod ( t ( M ) , I ) = max m M f Δ ( t ( m ) ) = max m M f min i I d ( t ( m ) , i ) .
d e = I x 4 4 h I x 3 + 6 h 2 I x 2 4 h 3 I x 1 a m 4 + I y 4 4 k I y 3 b m 4 + 6 k 2 I y 2 4 k 3 I y 1 b m 4 2 ( I x 2 2 h I x 1 + h 2 ( θ 2 θ 1 ) ) a m 2 2 ( I y 2 2 k I y 1 + k 2 ( θ 2 θ 1 ) ) b m 2 + I x 2 y 2 + k 2 I x 2 2 k I x 2 y 1 a m 2 b m 2 2 h I x 1 y 2 2 h k 2 I x 1 + 4 h k I x 1 y 1 + h 2 I y 2 2 h 2 k I y 1 a m 2 b m 2 + ( θ 2 θ 1 ) ( h 4 a m 4 + k 4 b m 4 + 1 + h 2 k 2 2 a m 2 b m 2 )
d e l = { ( u x 2 + g s ( α m ) h r ) 5 ( u x 1 + g s ( α m ) h r ) 5 } 5 u a m 4 + 1 5 v b m 4 { ( v x 2 + g c ( α m ) k r ) 5 ( v x 1 + g c ( α m ) k r ) 5 } 2 3 u a m 2 { ( u x 2 + g s ( α m ) h r ) 3 ( u x 1 + g s ( α m ) h r ) 3 } 2 3 v b m 2 { ( v x 2 + g c ( α m ) k r ) 3 ( v x 1 + g c ( α m ) k r ) 3 } + { x 2 x 1 } + 1 2 a m 2 b m 2 { u 2 v 2 ( x 2 5 x 1 5 5 ) + ( x 2 4 x 1 4 4 ) [ ( 2 u 2 v c ( α m ) g + 2 u v 2 g s ( α m ) ) ( 2 u 2 k r v + 2 h r u v 2 ) ] + [ ( u 2 g 2 c 2 ( α m ) ) + ( u 2 k r 2 ) ( 2 u 2 k r g c ( α m ) ) + ( v 2 g 2 s 2 ( α m ) ) + ( h r 2 v 2 ) + ( 4 u v g 2 c ( α m ) s ( α m ) ) ( 4 u k r v g s ( α m ) ) ( 4 u v h r g c ( α m ) ) + ( 4 h r u k r v ) ( 2 h r g v 2 s ( α m ) ) ] ( x 2 3 x 1 3 3 ) + [ ( g 4 s 2 ( α m ) c 2 ( α m ) ) + ( k r 2 g 2 s 2 ( α m ) ) ( 2 k r g 3 s 2 ( α m ) c ( α m ) ) + ( h r 2 g 2 c 2 ( α m ) ) + ( h r 2 k r 2 ) ( 2 k r g h r 2 c ( α m ) ) ( 2 h r g 3 c 2 ( α m ) s ( α m ) ) ( 2 h r g k r 2 s ( α m ) ) + ( 4 k r h r g 2 c ( α m ) s ( α m ) ) ] ( x 2 x 1 ) + [ ( 2 v g 3 s 2 ( α m ) c ( α m ) ) ( 2 k r v g 2 s 2 ( α m ) ) ( 2 k r h r 2 v ) + ( 2 v h r 2 g c ( α m ) ) + ( 2 u g 3 c 2 ( α m ) s ( α m ) ) + ( 2 u k r 2 g s ( α m ) ) ( 4 k r u g 2 c ( α m ) s ( α m ) ) ( 4 h r v g 2 c ( α m ) s ( α m ) ) ( 2 h r u g 2 c 2 ( α m ) ) ( 2 h r u k r 2 ) + ( 4 h r u k r g c ( α m ) ) + ( 4 k r v h r g s ( α m ) ) ] ( x 2 2 x 1 2 2 ) } .

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