3D occlusion recovery using few cameras

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Keck, Mark ^a   Davis, James W ^a  

^a The Ohio State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CAMERAS; COMPUTER VISION; ENERGY EFFICIENCY; FACE RECOGNITION; FEATURE EXTRACTION; IMAGE PROCESSING; LEARNING SYSTEMS; PATTERN RECOGNITION; THREE DIMENSIONAL;

3D TRACKING SYSTEM; AND MODELING; ENERGY FUNCTIONS; GRAPH CUTS; ITERATIVE LEARNING; ITERATIVE PROCEDURES; MULTI CAMERAS; OCCLUSION RECOVERY; STATIC OCCLUSIONS;

THREE DIMENSIONAL COMPUTER GRAPHICS;

EID: 51949101447     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587513     Document Type: Conference Paper

References (21)

1. N. Apostoloff and A. Fitzgibbon. Learning spatiotemporal T-junctions for occlusion detection. In Proc. Comp. Vis. and Pattern Rec., pages 553-559, 2005.
2. I. Biederman. Recognition-by-components: A theory of human image understanding. Pyschological Review, pages 115-147, 1987.
3. A. Broadhurst and R. Cipolla. The applications of uncalibrated occlusion junctions. In Proc. Brit. Mach. Vis. Conf., pages 245-254, 1999.
4. G. Cheung, S. Baker, and T. Kanade. Visual hull alignment and refinement across time: A 3d reconstruction algorithm combining shape-from-silhouette with stereo. In Proc. Comp. Vis. and Pattern Rec., 2003.
5. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Patt. Analy. and Mach. Intell., may 2002.
6. D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. IEEE Trans. Patt. Analy. and Mach. Intell., may 2003.
7. S. Dockstader and A. M. Tekalp. Multiple camera fusion for multi-object tracking. In Proc. of the IEEE Wkshp. on Multi-Object Tracking, 2001.
8. L. Guan et al. Visual hull construction in the presence of partial occlusion. In 3rd Int'l. Symp. on 3D Data Proc., Vis., and Transmission, 2006.
9. L. Guan, J.-S. Franco, and M. Pollefeys. 3D occlusion inference from silhouette cues. In Proc. Comp. Vis. and Pattern Rec., 2007.
10. Y. Huang and I. Essa. Tracking multiple objects through occlusions. In Proc. Comp. Vis. and Pattern Rec., 2005.
11. V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? IEEE Trans. Patt. Analy. and Mach. Intell., February 2004.
12. A. Laurentini. How far 3d shapes can be understood from 2d silhouettes. IEEE Trans. Patt. Analy. and Mach. Intell., 17(2):188-195, 1995.
13. P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Patt. Analy. and Mach. Intell., July 1990.
14. M. Pollefeys et al. Visual modeling with a hand-held camera. Int. J. of Comp. Vis., 59(3):207-232, 2004.
15. A. Senior et al. Appearance models for occlusion handling. In Proc. Int. Wkshp. on Perf. Eval. of Tracking and Surveillance, 2001.
16. V. Sharma and J. W. Davis. Integrating appearance and motion cues for simultaneous detection and segmentation of pedestrians. In Proc. Int. Conf. Comp. Vis., 2007.
17. Y. Sheikh and M. Shah. Bayesian object detection in dynamic scenes. In Proc. Comp. Vis. and Pattern Rec., pages 74-79, 2005.
18. J. G. Siek, L.-Q. Lee, and A. Lumsdaine. The Boost Graph Library: User Guide and Reference Manual. Addison-Wesley, Reading, MA, 2001.
19. D. Snow, P. Viola, and R. Zabih. Exact voxel occupancy with graph cuts. In Proc. Comp. Vis. and Pattern Rec., pages 345-352, 2000.
20. A. Tyagi et al. Fusion of multiple camera views for kernel-based 3D tracking. In Proc. Wkshp. Motion and Video Computing, February 2007.
21. Y. Zhou and H. Tao. A background layer model for object tracking through occlusion. In Proc. Int. Conf. Comp. Vis., 2003.