The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Stereo Correspondence by Dynamic Programming on a Tree
Abstract
Dynamic programming on a scanline is one of the oldest and still popular methods for stereo correspondence. While efficient, its performance is far from the state of the art because the vertical consistency between the scanlines is not enforced. We re-examine the use of dynamic programming for stereo correspondence by applying it to a tree structure, as opposed to the individual scanlines. The nodes of this tree are all the image pixels, but only the "most important" edges of the 4 connected neighbourhood system are included. Thus our algorithm is truly a global optimization method because disparity estimate at one pixel depends on the disparity estimates at all the other pixels, unlike the scanline based methods. We evaluate our algorithm on the benchmark Middlebury database. The algorithm is very fast; it takes only a fraction of a second for a typical image. The results are considerably better than that of the scanline based methods. While the results are not the state of the art, our algorithm offers a good trade off in terms of accuracy and computational efficiency.
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Rayward-Smith, V. J. (1991) · Journal of the Operational Research Society · doi:10.1057/jors.1991.155 · Cited by 16,821
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Pictorial Structures for Object Recognition
Felzenszwalb, P. F., & Huttenlocher, D. P. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042934.15159.49 · Cited by 2,180
Blake, A., & Zisserman, A. (1987) · The MIT Press eBooks · doi:10.7551/mitpress/7132.001.0001 · Cited by 2,105
Distance transformations in digital images
Borgefors, G. (1986) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(86)80047-0 · Cited by 1,898
Efficient Belief Propagation for Early Vision
Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7899-4 · Cited by 1,345
Stereo matching using belief propagation
Sun, J., Zheng, N.-N., & Shum, H.-Y. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1206509 · Cited by 1,271
Convergent Tree-Reweighted Message Passing for Energy Minimization
Kolmogorov, V. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.200 · Cited by 1,095
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Robinson, E. C., Jbabdi, S., Glasser, M. F., Andersson, J., Burgess, G. C., Harms, M. P., Smith, S. M., Van Essen, D. C., & Jenkinson, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.069 · Cited by 640
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Rhemann, C., Hosni, A., Bleyer, M., Rother, C., & Gelautz, M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995372 · Cited by 485
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Review of stereo vision algorithms and their suitability for resource-limited systems
Tippetts, B., Lee, D. J., Lillywhite, K., & Archibald, J. (2016) · Journal of Real-Time Image Processing · doi:10.1007/s11554-012-0313-2 · Cited by 274
Metadata
- Authors
- Veksler, O.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 2
- Pages
- 384-390
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2107017043
- Cited by
- 295
- References
- 22
- FWCI
- 12.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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