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5. Koschan, A.: Dense Stereo Correspondence Using Polychromatic Block Matching. In:
Chetverikov, D., Kropatsch, W.G. (eds.) CAIP 1993. LNCS, vol. 719, pp. 538-542.
Springer, Heidelberg (1993)
6. Chambon, S., Crouzil, A.: Color stereo matching using correlation measures. In: Com-
plex Systems Intelligence and Modern Technological Applications, Cherbourg, France,
pp. 520-525 (2004)
7. Bleyer, M., Chambon, S., Poppe, U., Gelautz, M.: Evaluation of different methods for
using colour information in global stereo matching approaches. In: The Congress of
the International Society for Photogrammetry and Remote Sensing, Beijing, Chine (July
2008)
8. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge
University Press, Cambridge (2004)
9. Fusiello, A., Roberto, V., Trucco, E.: A compact algorithm for rectification of stereo
pairs. Machine Vision and Applications 12(1), 16-22 (2000)
10. Kanade, T., Okutomi, M.: A Stereo Matching Algorithm with an Adaptive Window:
Theory and Experiment. IEEE Transactions on Pattern Analysis and Machine Intelli-
gence 16(9), 920-932 (1994)
11. Veksler, O.: Stereo matching by compact windows via minimum ratio cycle. In: Proc.
Int. Conf. on Computer Vision, Vancouver, BC, Canada, July 2001, vol. 1, pp. 540-547
(2001)
12. Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In:
Proc. Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii, vol. 1, pp.
156-161 (2002)
13. Fusiello, A., Roberto, V., Trucco, E.: Symmetric stereo with multiple windowing. Int.
Journal of Pattern Recognition and Artificial Intelligence 14(8), 1053-1066 (2000)
14. Wei, Y., Quan, L.: Region-based Progressive Stereo Matching. In: Proc. Computer Vision
and Pattern Recognition, Washington, US, June 2004, vol. 1, pp. 106-113 (2004)
15. Kim, C., Lee, K.M., Choi, B.T., Lee, S.U.: A Dense Stereo Matching Using Two-Pass
Dynamic Programming with Generalized Ground Control Points. In: Proc. Computer
Vision and Pattern Recognition, San Diego, US, June 2005, vol. 2, pp. 1075-1082 (2005)
16. Bleyer, M., Gelautz, M.: A layered stereo algorithm using image segmentation and global
visibility constraints. In: Proc. Int. Conf. on Image Processing, Singapour, October 2004,
vol. 5, pp. 2997-3000 (2004)
17. Zhang, Y., Kambhamettu, C.: Stereo Matching with Segmentation-based Cooperation.
In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351,
pp. 556-571. Springer, Heidelberg (2002)
18. Bleyer, M., Gelautz, M.: Graph-based surface reconstruction from stereo pairs using im-
age segmentation. In: Videometrics VIII, San José, US, January 2005, vol. SPIE-5665,
pp. 288-299 (2005)
19. Tappen, M.F., Freeman, W.T.: Comparison of Graph Cuts with Belief Propagation for
Stereo, using Identical MRF Parameters. In: Proc. Int. Conf. Computer Vision, Nice,
France, October 2003, vol. 2, pp. 900-907 (2003)
20. Alvarez, L., Deriche, R., Sanchez, J., Weickert, J.: Dense disparity map estimation re-
specting image discontinuities: A PDE and scale-space based approach. Journal of Visual
Communication and Image Representation 13, 3-21 (2002)
21. Slesareva, N., Bruhn, A., Weickert, J.: Optic flow goes stereo: A variational method
for estimating discontinuity preserving dense disparity maps. In: Kropatsch, W.G., Sab-
latnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 33-40. Springer,
Heidelberg (2005)
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