Classification and evaluation of cost aggregation methods for stereo correspondence

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

Volumn , Issue , 2008, Pages

  Tombari, Federico ^a   Mattoccia, Stefano ^a   Stefano, Luigi Di ^a   Addimanda, Elisa ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

AGGREGATION METHODS; COMPUTATIONAL REQUIREMENTS; DATA SETS; EXPERIMENTAL EVALUATIONS; GLOBAL ALGORITHMS; STEREO CORRESPONDENCE;

AGGLOMERATION; ARTIFICIAL INTELLIGENCE; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; IMAGE SEGMENTATION; PATTERN RECOGNITION; STANDARDS;

COMPUTATIONAL METHODS;

EID: 51949093661     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587677     Document Type: Conference Paper

References (33)

1. S. Adhyapak, N. Kehtarnavaz, and M. Nadin. Stereo matching via selective multiple windows. Journ. of Electronic Imaging, 16(1):013012, 2007.
2. R. Arnold. Automated stereo perception. Technical Report AIM-351, Artificial Intelligence Laboratory, Stanford University, 1983.
3. S. Birchfield and C. Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. PAMI, 20(4):401-406, 1998.
4. A. Bobick and S. Intille. Large occlusion stereo. IJCV, 33(3):181-200, 1999.
5. Y. Boykov, O. Veksler, and R. Zabih. A variable window approach to early vision. IEEE Trans. PAMI, 20(12):1283-1294, 1998.
6. M. Z. Brown, D. Burschka, and G. D. Hager. Advances in computational stereo. IEEE Trans. PAMI, 25(8):993-1008, 2003.
7. S. Chan, Y. Wong, and J. Daniel. Dense stereo correspondence based on recursive adaptive size multi-windowing. In Proc. Image and Vision Computing New Zealand, volume 1, pages 256-260, 2003.
8. T. Darrel. A radial cumulative similarity transform for robust image correspondence. In Proc. Conf. Computer Vision and Pattern Recognition, pages 656-662, 1998.
9. C. Demoulin and M. Van Droogenbroeck. A method based on multiple adaptive windows to improve the determination of disparity maps. In Proc. IEEE Workshop on Circuit, Systems and Signal Processing, pages 615-618, 2005.
10. A. Fusiello, V. Roberto, and E. Trucco. Symmetric stereo with multiple windowing. Int. Journal of Pattern Recognition and Artificial Intelligence, 14(8):1053-1066, 2000.
11. D. Geiger, B. Ladendorf, and A. Yuille. Occlusions and binocular stereo. IJCV, 14(3):211-226, 1995.
12. M. Gerrits and P. Bekaert. Local stereo matching with segmentation-based outlier rejection. In Proc. Conf. Computer and Robot Vision, pages 66-66, 2006.
13. M. Gong and R. Yang. Image-gradient-guided real-time stereo on graphics hardware. In Proc. Conf. 3D Digital Imaging and Modeling (3DIM), pages 548-555, 2005.
14. M. Gong, R. Yang, W. Liang, and M. Gong. A performance study on different cost aggregation approaches used in realtime stereo matching. IJCV, 75(2):283-296, 2007.
15. H. Hirschmuller, P. Innocent, and J. Garibaldi. Real-time correlation-based stereo vision with reduced border errors. IJCV, 47:1-3, 2002.
16. H. Hirschmuller and D. Scharstein. Evaluation of cost functions for stereo matching. In Proc. Conf. Computer Vision and Pattern Recognition, volume 1, pages 1-8, 2007.
17. S. Kang, R. Szeliski, and J. Chai. Handling occlusions in dense multi-view stereo. In Proc. Conf. Computer Vision and Pattern Recognition, pages 103-110, 2001.
18. J. Kim, K. Lee, B. Choi, and S. Lee. A dense stereo matching using two-pass dynamic programming with generalized ground control points. In Proc. Conf. Computer Vision and Pattern Recognition, pages 1075-1082, 2005.
19. M. Levine, D. O'Handley, and G. Yagi. Computer determination of depth maps. Computer Graphics and Image Processing, 2:131-150, 1973.
20. S. Mattoccia, F. Tombari, and L. Di Stefano. Stereo vision enabling precise border localization within a scanline optimization framework. In Proc. Asian Conf. on Computer Vision, pages 517-527, 2007.
21. M. Okutomi and T. Kanade. A locally adaptive window for signal matching. IJCV, 7(2):143-162, 1992.
22. M. Okutomi, Y. Katayama, and S. Oka. A simple stereo algorithm to recover precise object boundaries and smooth surfaces. IJCV, 47(1-3):261-273, 2002.
23. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1/2/3):7-42, 2002.
24. H. Tao, H. Sawheny, and R. Kumar. A global matching framework for stereo computation. In Proc. Int. Conf. Computer Vision, volume 1, pages 532-539, 2001.
25. F. Tombari, S. Mattoccia, and L. Di Stefano. Segmentation-based adaptive support for accurate stereo correspondence. In Proc. Pacific-Rim Symposium on Image and Video Technology, 2007.
26. O. Veksler. Stereo matching by compact windows via minimum ratio cycle. In Proc. Int. Conf. Computer Vision, volume 1, pages 540-547, 2001.
27. O. Veksler. Fast variable window for stereo correspondence using integral images. In Proc. Conf. Computer Vision and Pattern Recognition, pages 556-561, 2003.
28. L. Wang, M. Liao, M. Gong, R. Yang, and D. Nister. High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In Proc. Int. Symp. 3D Data Proc., Vis. and Transm. (3DPVT), pages 798-805, 2006.
29. Y. Xu, D. Wang, T. Feng, and H. Shum. Stereo computation using radial adaptive windows. In Int. Conf. Pattern Recognition, volume 3, pages 595- 598, 2002.
30. K. Yoon and I. Kweon. Adaptive support-weight approach for correspondence search. IEEE Trans. PAMI, 28(4):650-656, 2006.
31. K. Yoon and I. Kweon. Stereo matching with symmetric cost functions. In Proc. Conf. Computer Vision and Pattern Recognition, volume 2, pages 2371-2377, 2006.
32. K. Yoon and I. Kweon. Stereo matching with the distinctive similarity measure. In Proc. Int. Conf. Computer Vision (ICCV'07), 2007.
33. C. Zitnick and T. Kanade. A cooperative algorithm for stereo matching and occlusion detection. IEEE Trans. PAMI, 22(7):675-684, 2000.