Hierarchical stereo with thin structures and transparency

Proceedings of the 5th Canadian Conference on Computer and Robot Vision, CRV 2008

Volumn , Issue , 2008, Pages 97-104

  Sizintsev, Mikhail ^a  

^a YORK UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; OPTICAL PROPERTIES; ROBOTICS; TRANSPARENCY;

COMPUTER AND ROBOT VISION; EXPERIMENTAL EVALUATIONS; GRAPH CUTS; IMAGE PYRAMIDS; LAPLACIAN PYRAMIDS; LOCAL BLOCK MATCHING; LOW-RESOLUTION IMAGES; SEARCH SPACES; SPATIAL FREQUENCIES; SPEED UPS; STEREO ALGORITHMS; THIN STRUCTURES;

LAPLACE TRANSFORMS;

EID: 52049106373     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CRV.2008.8     Document Type: Conference Paper

References (21)

1. P. Anandan. A computational framework and an algorithm for the measurement of visual motion. IJCV, 2:283-301, 1989.
2. J. R. Bergen, P. J. Burt, R. Hingorani, and S. Peleg. A three-frame algorithm for estimating two-component image motion. TPAMI, 14(9):886-896, 1992.
3. M. J. Black and P. Anandan. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. CVIU, 61(1):75-104, 1996.
4. M. Borga and H. Knutsson. Estimating multiple depths in semi-transparent stereo images. In SCIA, 1999.
5. M. Z. Brown, D. Burschka, and G. D. Hager. Advances in computational stereo. TPAMI, 25(8):993-1008, 2003.
6. P. J. Burt and E. H. Adelson. The Laplacian pyramid as a compact image code. Trans. Comm., 31(4):532-540, 1983.
7. CMU VASC Image Database. http://www.vasc.ri.cmu.edu/idb.
8. N. Cornelis and L. van Gool. Real-time connectivity constrained depth map combination using programmable graphics hardware. In CVPR, pages 1099-1104, 2005.
9. J. Gluckman. Higher order whitening of natural images. CVPR, pages 354-360, 2005.
10. J. Gluckman. Higher order image pyramids. In ECCV, pages 308-320, 2006.
11. M. Irani, B. Rousso, and S. Peleg. Computing occluding and transparent motions. IJCV, 12(1):5-16, 1994.
12. V. Kolmogorov and R. Zabih. Computing visual correspondence with occlusions using graph cuts. In ICCV, pages 508-515, 2001.
13. Middlebury College Stereo Vision Page. http://www.middlebury.edu/stereo/.
14. D. Scharstein and R. Szeliski. Taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47:7-42, 2002.
15. M. Shizawa. Direct estimation of multiple disparities for transparent multiple surfaces in binocular stereo. In ICCV, pages 447-454, 1993.
16. M. Sizintsev and R. P. Wildes. Efficient stereo with accurate 3-D boundaries. In BMVC, pages 237-246, 2006.
17. C. Stewart. Robust parameter estimation in computer vision. 41(3), 1999.
18. C.Sun. Fast stereo matching using rectangular subregioning and 3D maximum-surface techniques. IJCV, 47:99-117, 2002.
19. Y. Tsin, S. B. Kang, and R. Szeliski. Stereo matching with linear superposition of layers. TPAMI, 28(2):290-301, 2006.
20. H. R. Wilson, R. Blake, and D. L. Halpern. Coarse spatial scales constrain the range of binocular fusion on fine scales. JOSA, pages 229-236, 1991.
21. R. Yang and M. Pollefeys. Multi-resolution real-time stereo on commodity graphics hardware. In CVPR, pages 211-217, 2003.