A New Stereo Matching Method Based on the Adaptive Support-Weight Window - Multimedia and Signal Processing - page 153

References
1. Scharstein, D., Szeliski, R., Zabih, R.: A Taxonomy and Evaluation of Dense Two-Frame
Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1), 7-42
(2002)
2. Tappen, M.F., Freeman, W.T.: Comparison of graph cuts with belief propagation for
stereo, using identical MRF parameters. In: ICCV, vol. 2, pp. 900-907 (2003)
3. Brown, M.Z., Burschka, D., Hager, G.D.: Advances in Computational Stereo. IEEE
Transactions on Pattern Analysis and Machine Intelligence 25(8), 993-1008 (2003)
4. Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Classification and Evaluation
of Cost Aggregation Methods for Stereo Correspondence. In: IEEE Conference on
Computer Vision and Pattern Recognition, pp. 1-8 (2008)
5. Bobick, A.F., Intille, S.S.: Large occlusion stereo. International Journal of Computer
Vision 33(3), 181-200 (1999)
6. Kanade, T., Okutomi, M.: A Stereo Matching Algorithm with an Adaptive Window:
Theory and Experiment. IEEE Transactions on Pattern Analysis and Machine
Intelligence 16(9), 920-932 (1994)
7. Boykov, Y., Veksler, O., Zabih, R.: A Variable Window Approach to Early Vision. IEEE
Trans. PAMI 20(12), 128-1294 (1998)
8. Veksler, O.: Stereo Correspondence with Compact Windows via Minimum Ratio Cycle.
IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12) (2002)
9. Adhyapak, S., Kehtarnavaz, N., Nadin, M.: Stereo Matching via selective multiple
windows. Journal of Electronic Imaging 16(1) (2007)
10. Veksler, O.: Fast variable window for stereo correspondence using integral images. In:
Proc. Conf. on Computer Vision and Pattern Recognition, pp. 556-561 (2003)
11. Gerrits, M., Bekaert, P.: Local Stereo Matching with Segmentation-based Outlier
Rejection. In: Proc. Canadian Conf. on Computer and Robot Vision, pp. 66-73 (2006)
12. Xu, Y., Wang, D., Feng, T., Shum, H.Y.: Stereo computation using radial adaptive
windows. In: IEEE International Conference on Pattern Recognition, vol. 3 (2002)
13. Yoon, K.J., Kweon, I.S.: Adaptive Support-Weight Approach for Correspondence Search.
IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 650-656 (2006)
14. Tombari, F., Mattoccia, S., Di Stefano, L.: Segmentation-Based Adaptive Support for
Accurate Stereo Correspondence. In: Mery, D., Rueda, L. (eds.) PSIVT 2007. LNCS,
vol. 4872, pp. 427-438. Springer, Heidelberg (2007)
15. Gu, Z., Su, X.Y., Liu, Y.K., Zhang, Q.: Local Stereo Matching with Adaptive Support-
weight, Rank Transform and Disparity Calibration. Pattern Recognition Letters 29(9)
(2008)
16. Hosni, A., Bleyer, M., Gelautz, M., Rhemann, C.: Local Stereo Matching Using Geodesic
Support Weights. In: IEEE International Conference on Image Processing, pp. 2093-2096
(2009)
17. Mattoccia, S., Giardino, S., Gambini, A.: Accurate and Efficient Cost Aggregation
Strategy for Stereo Correspondence Based on Approximated Joint Bilateral Filtering. In:
Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp.
371-380. Springer, Heidelberg (2010)
18. The Middlebury Computer Vision Pages, http://vision.middlebury.edu
19. Computer vision laboratory,
http://www.vision.deis.unibo.it/spe/SPEresultsTAD.aspx
20. Mattoccia, S.: A locally global approach to stereo correspondence. In: IEEE Workshop on
3D Digital Imaging and Modeling (2009)