Matching with shape contexts

Modeling and Simulation in Science, Engineering and Technology

Volumn , Issue 9780817643768, 2006, Pages 81-105

  Belongie, Serge ^a   Mori, Greg ^b   Malik, Jitendra ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b SIMON FRASER UNIVERSITY   (Canada)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

object recognition; shape correspondence; Shape distance; thin plate spline (TPS)

Indexed keywords

EID: 84920259734     PISSN: 21643679     EISSN: 21643725     Source Type: Book Series    
DOI: 10.1007/0-8176-4481-4_4     Document Type: Chapter

References (44)

1. Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(11):1300–1305, November 1997.
2. S. Belongie, J. Malik, and J. Puzicha. Matching shapes. In Proc. 8th Int. Conf. Computer Vision, volume 1, pages 454–461, July 2001.
3. S. Belongie, J. Malik, and J. Puzicha. Shape context: A new descriptor for shape matching and object recognition. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, pages 831–837, 2001.
4. S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(4):509–522, April 2002.
5. A. Berg, T. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 26–33, San Diego, CA, June 2005.
6. A. Berg and J. Malik. Geometric blur for template matching. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 607–614, Kauai, HI, December 2001.
7. P. J. Bickel. A distribution free version of the Smirnov two-sample test in the multivariate case. Annals of Mathematical Statistics, 40:1–23, 1969.
8. F. L. Bookstein. Principal warps: thin-plate splines and decomposition of deformations. IEEE Trans. Pattern Analysis and Machine Intelligence, 11(6):567-585, June 1989.
9. F. L. Bookstein. Morphometric tools for landmark data: geometry and biology. Cambridge Univ. Press, London, 1991.
10. H. Chui and A. Rangarajan. A new algorithm for non-rigid point matching. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 44–51, Hilton Head, SC, June 2000.
11. D. DeCoste and B. Schölkopf. Training invariant support vector machines. Machine Learning, 46(1–3):161–190, January-February-March 2002.
12. G. Dorko and C. Schmid. Selection of scale invariant neighborhoods for object class recognition. In Proc. 9th Int. Conf. Computer Vision, pages 634–640, 2003.
13. J. Duchon. Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In W. Schempp and K. Zeller, editors, Constructive Theory of Functions of Several Variables, pages 85–100. Springer-Verlag, Berlin, 1977.
14. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 264–271, Madison, WI, June 2003.
15. M. Fischler and R. Elschlager. The representation and matching of pictorial structures. IEEE Trans. Computers, C-22(1):67–92, 1973.
16. A. Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik. Recognizing objects in range data using regional point descriptors. In Proc. 8th Europ. Conf. Comput. Vision, volume 3, pages 224–237, 2004.
17. F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7(2):219–269, 1995.
18. U. Grenander, Y. Chow, and D. Keenan. HANDS: A Pattern Theoretic Study of Biological Shapes. Springer, New York, 1991.
19. H. Guo, A. Rangarajan, S. Joshi, and L. Younes. A new joint clustering and diffeomorphism estimation algorithm for non-rigid shape matching. In IEEE Workshop on Articulated and Non-rigid motion (ANM), Washington, DC, 2004.
20. A. Ben Hamza and H. Krim. Geodesic object representation and recognition. In Discrete Geometry for Computer Imagery: 11th International Conference, DGCI, pages 378–387, 2003.
21. S. Jeannin and M. Bober. Description of core experiments for MPEG-7 motion/ shape. Technical Report ISO/IEC JTC 1/SC 29/WG 11 MPEG99/N2690, MPEG-7, Seoul, March 1999.
22. A. E. Johnson and M. Hebert. Recognizing objects by matching oriented points. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pages 684–689, San Juan, Puerto Rico, 1997.
23. D. Jones and J. Malik. Computational framework to determining stereo correspondence from a set of linear spatial filters. Image and Vision Computing, 10(10):699–708, Dec. 1992.
24. R. Jonker and A. Volgenant. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing, 38:325–340, 1987.
25. M. Lades, C. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R. Wurtz, and W. Konen. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Computers, 42(3):300–311, March 1993.
26. L. J. Latecki, R. Lakämper, and U. Eckhardt. Shape descriptors for non-rigid shapes with a single closed contour. In Proc. IEEE Conf. Comput. Vision and Pattern Recognition, pages 424–429, 2000.
27. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, November 1998.
28. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision, 60(2):91–110, 2004.
29. D. Martin, C. Fowlkes, and J. Malik. Learning to find brightness and texture boundaries in natural images. NIPS, 2002.
30. J. Meinguet. Multivariate interpolation at arbitrary points made simple. J. Appl. Math. Phys. (ZAMP), 5:439–468, 1979.
31. G. Mori, S. Belongie, and J. Malik. Shape contexts enable efficient retrieval of similar shapes. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Kauai, HI, December 2001.
32. G. Mori and J. Malik. Recognizing objects in adversarial clutter: Breaking a visual captcha. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., volume 1, pages 134–141, Madison, WI, 2003.
33. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. Shape distributions. ACM Transactions on Graphics, 21(4):807–832, October 2002.
34. C. Papadimitriou and K. Stieglitz. Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Upper Saddle River, NJ, 1982.
35. M. J. D. Powell. A thin plate spline method for mapping curves into curves in two dimensions. In Computational Techniques and Applications (CTAC95), Melbourne, Australia, 1995.
36. B. D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.
37. E. Rosch. Natural categories. Cognitive Psychology, 4(3):328–350, 1973.
38. E. Rosch, C. B. Mervis, W. D. Gray, D. M. Johnson, and P. Boyes-Braem. Basic objects in natural categories. Cognitive Psychology, 8(3):382–439, 1976.
39. J. G. Snodgrass and M. Vanderwart. A standardized set of 260 pictures: Norms for name agreement, familiarity and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 6:174–215, 1980.
40. A. Thayananthan, B. Stenger, P. H. S. Torr, and R. Cipolla. Shape context and chamfer matching in cluttered scenes. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn., volume I, pages 127–133, Madison, WI, June 2003.
41. D’Arcy Wentworth Thompson. On Growth and Form. Cambridge University Press, London, 1917.
42. L. von Ahn, M. Blum, and J. Langford. Telling humans and computers apart (automatically). CMU Tech Report CMU-CS-02-117, February 2002.
43. G. Wahba. Spline Models for Observational Data. SIAM, 1990.
44. A. Yuille. Deformable templates for face recognition. J. Cognitive Neuroscience, 3(1):59–71, 1991.