Shape matching and object recognition using low distortion correspondences

Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

Volumn I, Issue , 2005, Pages 26-33

  Berg, Alexander C ^a   Berg, Tamara L ^a   Malik, Jitendra ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; FORMABILITY; IMAGE ANALYSIS; INTEGER PROGRAMMING; PROBLEM SOLVING; SET THEORY;

INTRACLASS VARIATIONS; LOW DISTORTION CORRESPONDENCES; PROFILE FACES; SHAPE MATCHING;

OBJECT RECOGNITION;

EID: 24644502276     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.320     Document Type: Conference Paper

References (33)

1. Caltech 101 dataset www.vision.caltech.edu/feifeili/101_ObjectCategories.
2. Y. Amit. D. Geman, and K. Wilder. Joint induction of shape features and tree classifiers. IEEE Trans, PAMI, 19( 11): 1300-1305, November 1997.
3. S. Belongie. J. Malik. and J. Puzicha. Matching shapes. In ICCV, pages 1.454-461, 2001.
4. A. C. Berg and J. Malik. Geometric blur for template matching. In CVPR, pages 607-614, 2001.
5. T. L. Berg, A. C. Berg, J. Edwards, M. Maire, R. White, Y. W. Teh, E. Learned-Miller, and D. A. Forsyth. Names and faces in the news. In CVPR, pages 848-854, 2004.
6. H. Chui and A. Rangarajan. A new point matching algorithm for non-rigid registration. CVIU, 89:114-141, 2003.
7. E. Fei-Fei, R. Fergus, and P. Perona. A bayesian approach to unsupervised one-shot learning of object categories. In ICCV. pages 1134-1141. 2003.
8. E. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In CVPR, Workshop on Generative-Model Based Vision, 2004.
9. R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In CVPR. pages 264-271, 2003.
10. M. Fischler and R. Elschlager. The representation and matching of pictorial structures. IEEE Trans. Computers, C-22(l): 67-92. 1973.
11. D. Gavrila and V. Philomin. Real-time object detection for smart vehicles. In Proc. 7th Int. Conf. Computer Vision, pages 87-93, 1999.
12. U. Grenander. Y. Chow, and D. Keenan. HANDS: A Pattern Theoretic Study Of Biological Shapes. Springer. 1991.
13. A. Holub, M. Welling, and P. Perona. M.S.R.I. March 2005.
14. D. Huttenlocher. G. Klanderman. and W. Rucklidge. Comparing images using the Hausdorff distance. IEEE Trans. PAMI. 15(9):850-863, Sept. 1993.
15. 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.
16. Y. Lamdan, J. Schwartz, and H. Wolfson. Affine invariant model-based object recognition. IEEE Trans. Robotics and Automation, 6:578-589. 1990.
17. T. Leung, M. Burl, and P. Perona. Finding faces in cluttered scenes using random labeled graph matching. In Proc. 5th Int. Conf. Computer Vision, pages 637-644, 1995.
18. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
19. J. Maciel and J. Costeira. A global solution to sparse correspondence problems. PAMI. 25(2): 187-199. 2003.
20. D. Martin, C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI, 26(5):530-549, 2004.
21. K. Mikolajczyk, Detection of local features invariant to affines transformations. PhD thesis, INPG, 2002.
22. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. In CVPR, pages 257-263, 2003.
23. G. Mori, S. Belongie, and J. Malik. Shape contexts enable efficient retrieval of similar shapes. In CVPR, volume 1, pages 723-730, 2001.
24. M. Morrone and D. Burr. Feature detection in human vision: A phase dependent energy model. Proc. Royal Soc. of London B, 235:221-245, 1988.
25. M. J. D. Powell. A thin plate spline method for mapping curves into curves in two dimensions. In CTAC, Melbourne, Australia, 1995.
26. F. Rothganger, S. Lazebnik, C. Schmid. and J. Ponce. 3d object modeling and recognition using affine-invariant patches and multi-view spatial constraints. In CVPR, pages II:272275. 2003.
27. C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Trans. PAMI. 19(5):530-535, May 1997.
28. H. Schneiderman. Feature-centric evaluation for efficient cascaded object detection. In CVPR, pages 29-36, 2004.
29. H. Schneiderman and T. Kanade. A statistical method for 3d object detection applied to faces and cars. In CVPR. pages 746-751, 2000.
30. D.W.Thompson. On Growth and Form. Dover, 1917.
31. A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In CVPR, pages 762-769, 2004.
32. S. Ullman, M. Vidal-Naquet, and F. Sali. Visual features of intermediate complexity and their use in classification. Nat. Neur., 13:682-687, 2002.
33. P. Viola and M. Jones. Robust real-time object detection. 2nd Intl. Workshop on Statistical and Computational Theories of Vision, 2001.