Object detection using 2D spatial ordering constraints

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

Volumn II, Issue , 2005, Pages 711-718

  Li, Yan ^a   Tsin, Yanghai ^b   Genc, Yakup ^b   Kanade, Takeo ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; GRAPH THEORY; MATHEMATICAL MODELS; NUMERICAL ANALYSIS; RANDOM PROCESSES;

2D SPATIAL ORDERING CONSTRAINTS; 3D OBJECT MODELS; DELAUNAY TRIANGULATION; GRAPH MATCHING PROBLEMS;

FEATURE EXTRACTION;

EID: 24644438786     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.253     Document Type: Conference Paper

References (23)

1. S. Agarwal and D. Roth. Learning a sparse representation for object detection. In ECCV, pages 113-127, 2002.
2. Y. Amit, D. Geman, and K. Wilder. Joint induction of shape features and tree classifiers. PAMI, 19(11):1300-1305, 1997.
3. M. J. Black and A. Rangarajan. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. IJCV, 19(1):57-91, 1996.
4. A. Cross and E. Hancock. Graph matching with a dual-step EM algorithm. PAMI, 20(11):1236-1253, 1998.
5. R. Fergus, P. Perona, and Z. Zisserman. Object class recognition by unsupervised scale-invariant learning. In CVPR, 2003.
6. V. Ferrari, T. Tuytelaars, and L. Van Gool. Wide baseline multiple view correspondence. In CVPR, pages 718-725, 2003.
7. V. Ferrari, T. Tuytelaars, and L. Van Gool. Integrating multiple model views for object recognition. In CVPR, 2004.
8. W. T. Freeman and E. H. Adelson. The design and use of steerable filters. PAMI, 13(9):891-906, 1991.
9. I. K. Jung and S. Lacroix. A robust interest point matching algorithm, In ICCV, 2001.
10. T. Kadir and M. Brady. Scale, saliency and image description. IJCV, 45(2):83-105, 2001.
11. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts. PAMI, 26(2):147-159, 2004.
12. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory, 47(2):498-519, 2001.
13. D. G. Lowe. Distinctive image features from scale-invariant key-points. IJCV, 60(2):91-110, 2004.
14. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. In CVPR, 2003.
15. P. Moreels, M. Maire, and P. Perona. Recognition by probabilistic hypothesis construction. In ECCV, 2004.
16. Y. Ohta and T. Kanade. Stereo by intra- and inter-scanline search using dynamic programming. PAMI, 7(2):139-154, 1985.
17. F. Rothganger, S. Lazebnik, C. Schmid. and J. Ponce. 3D object modeling and recognition using affine-invariant patches and multiview spatial constraints. In CVPR, 2003.
18. F. Schaffalitzky and A. Zisserman. Multi-view matching for unordered image sets, or How do I organize my holiday snaps? In ECCV, 2002.
19. J. Sun, N. N. Zheng, and H. Y. Shum. Stereo matching using belief propagation. PAMI, 25(7):787-800, 2003.
20. S. Umeyama. An eigendecomposition approach to weighted graph matching problems. PAMI, 10(5):695-703, 1988.
21. L. Van Gool, T. Moons, and D. Ungureanu. Affine photometric invariants for planar intensity patterns. In ECCV, pages 642-651, 1996.
22. M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Technical Report 69, Department of Statistics, University of California, Berkeley, 2003.
23. J. S. Yedidia, W. T. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. Technical Report 2001-22, MERL, 2001.