Shape guided contour grouping with particle filters

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 2288-2295

  Lu, ChengEn ^a   Latecki, Longin Jan ^b   Adluru, Nagesh ^c   Yang, Xingwei ^d   Ling, Haibin ^d  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Temple University   (United States)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

^d TEMPLE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUTTERED IMAGES; CONTOUR GROUPING; EDGE IMAGE; FINE TUNING; OBJECT DETECTION; OBJECT DETECTION AND RECOGNITION; PARTICLE FILTER; SEARCH STRATEGIES; TRAINING DATA;

COMPUTER VISION; NONLINEAR FILTERING;

OBJECT RECOGNITION;

EID: 77953197515     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459446     Document Type: Conference Paper

References (34)

1. S. Belongie, J. Puzhicha, and J. Malik. Shape matching and object recognition using shape contexts. IEEE PAMI, 24:509-522, 2002.
2. G. Borgefors. Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE PAMI, 10(6):849-865, 1988.
3. S. Carlsson. Order structure, correspondence and shape based categories. In In Shape Contour and Grouping in Computer Vision.
4. J. Carpenter, P. Clifford, and P. Fearnhead. Building robust simulation-based filters for evolving data sets. Technical report, Dept. of Statistics, University of Oxford, 1999.
5. P. Felzenszwalb and D. McAllester. A min-cover approach for finding salient curves. In CVPRW: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, 2006.
6. P. F. Felzenszwalb and J. Schwartz. Hierarchical matching of deformable shapes. In CVPR, 2007.
7. V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid. Groups of adjacent contour segments for object detection. IEEE PAMI., 30(1):36-51, 2008.
8. V. Ferrari, F. Jurie, and C. Schmid. Accurate object detection with deformable shape models learnt from images. In CVPR, pages 1-8, 2007.
9. V. Ferrari, F. Jurie, and C. Schmid. From images to shape models for object detection. INRIA Technical Report, July 2008.
10. V. Ferrari, T. Tuytelaars, and L. J. V. Gool. Object detection by contour segment networks. In ECCV, 2006.
11. M. Galun, R. Basri, and A. Brandt. Multiscale edge detection and fiber enhancement using differences of oriented means. In ICCV, 2007.
12. N. Gordon, D. Salmond, and A. Smith. Novel approach to nonlinear/non-gaussian bayesian state estimation. In Radar and Signal Processing, IEE Proceedings of, volume 140, pages 107-113, 1993.
13. W. E. L. Grimson. The combinatorics of object recognition in cluttered environments using constrained search. Artif. Intell., 44(1-2):121-165, 1990.
14. W. E. L. Grimson. Object recognition by computer: the role of geometric constraints. MIT Press, Cambridge, MA, USA, 1990.
15. W. E. L. Grimson and T. Lozano-Pérez. Localizing overlapping parts by searching the interpretation tree. IEEE PAMI, 9(4), 1987.
16. D. Hoiem, A. Stein, A. A. Efros, and M. Hebert. Recovering occlusion boundaries from a single image. In ICCV, 2007.
17. P. D. Kovesi. MATLAB and Octave functions for computer vision and image processing.
18. B. Leibe, E. Seemann, and B. Schiele. Pedestrian detection in crowded scenes. In CVPR, 2005.
19. S. Maji and J. Malik. Object detection using a max-margin hough transform, 2009. CVPR.
20. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001.
21. G. McNeill and S. Vijayakumar. Part-based probabilistic point matching using equivalence constraints. In NIPS, pages 969-976, 2006.
22. A. Opelt, A. Pinz, and A. Zisserman. A boundary-fragment-model for object detection. In ECCV, 2006.
23. S. Ravishankar, A. Jain, and A. Mittal. Multi-stage contour based detection of deformable objects. In ECCV, 2008.
24. T. B. Sebastian, P. N. Klein, and B. B. Kimia. Recognition of shapes by editing their shock graphs. PAMI, 26(5):550-571, 2004.
25. J. Shotton, A. Blake, and R. Cipolla. Contour-based learning for object detection. In ICCV, 2005.
26. A. Stein, D. Hoiem, and M. Hebert. Learning to find object boundaries using motion cues. In ICCV, 2007.
27. A. Tamrakar and B. B. Kimia. No grouping left behind: From edges to curve fragments. In ICCV, 2007.
28. A. Thayananthan, B. Stenger, P. H. S. Torr, and R. Cipolla. Shape context and chamfer matching in cluttered scenes. In CVPR, pages 127-133, 2003.
29. S. Thrun, W. Burgard, and D. Fox. Probabilistic Robotics. The MIT Press Cambridge, 2005.
30. J. Thureson and S. Carlsson. Appearance based qualitative image description for object class recognition. In ECCV, 2004.
31. N. Trinh and B. B. Kimia. A symmetry-based generative model for shape. In ICCV, 2007.
32. L. Wang, J. Shi, G. Song, and I. Shen. Object detection combining recognition and segmentation. In ACCV07, pages 189-199, 2007.
33. Q. Zhu, G. Song, and J. Shi. Untangling cycles for contour grouping. In ICCV, 2007.
34. Q. Zhu, L. Wang, Y. Wu, and J. Shi. Contour context selection for object detection: A set-to-set contour matching approach. In ECCV, 2008.