T. Aydin and Y. S. , Stereo depth estimation using synchronous optimization with segment based regularization, Pattern Recognition Letters, vol.31, issue.15, pp.312389-2396, 2010.
DOI : 10.1016/j.patrec.2010.07.012

S. Beucher, Segmentation d'Images et Morphologie Mathématique, 1990.

S. Beucher, Maxima and minima : a review, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01403965

M. Bleyer and M. Gelautz, A layered stereo matching algorithm using image segmentation and global visibility constraints, ISPRS Journal of Photogrammetry and Remote Sensing, vol.59, issue.3, pp.128-150, 2005.
DOI : 10.1016/j.isprsjprs.2005.02.008

L. De-maeztu, A. Villanueva, and R. Cabeza, Near realtime stereo matching using geodesic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.2, pp.410-416, 2012.
DOI : 10.1109/tpami.2011.192

J. Delhomme, Applications de la théorie des variables régionalisées dans les sciences de l'eau, 1976.

P. Fua, A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine vision and applications, pp.35-49, 1993.
URL : https://hal.archives-ouvertes.fr/inria-00075191

A. Goshtasby, Similarity and Dissimilarity Measures, Image Registration, Advances in Computer Vision and Pattern Recognition, pp.7-66
DOI : 10.1007/978-1-4471-2458-0_2

M. J. Hannah, Computer Matching of Areas in Stereo Images, 1974.

H. Hirschmuller, Stereo processing by semiglobal matching and mutual information. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.30, issue.2, pp.328-341, 2008.
DOI : 10.1109/tpami.2007.1166

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.5238

R. Horaud and O. Monga, Vision par ordinateur : outils fondamentaux, chapter Vision stéréoscopique, Hermes, 1995.

S. Huq, A. Koschan, and M. Abidi, Occlusion filling in stereo: Theory and experiments, Computer Vision and Image Understanding, vol.117, issue.6, pp.688-704, 2013.
DOI : 10.1016/j.cviu.2013.01.008

P. Jaccard, Bulletin de la société vaudoise des sciences naturelles, 1901.

S. B. Kang, R. Szeliski, and J. Chai, Handling occlusions in dense multi-view stereo, Computer Vision and Pattern Recognition Proceedings of the 2001 IEEE Computer Society Conference on, p.103, 2001.

Y. Ohta and T. Kanade, Stereo by intra-and inter-scanline search using dynamic programming. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.2, pp.139-154, 1985.

S. Prince, Computer vision : models, learning, and inference , chapter Models for grids, 2012.
DOI : 10.1017/CBO9780511996504

D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), pp.7-42, 2002.
DOI : 10.1109/SMBV.2001.988771

K. Yamaguchi, T. Hazan, D. Mcallester, and R. Urtasun, Continuous Markov Random Fields for Robust Stereo Estimation, Computer Vision?ECCV 2012, pp.45-58, 2012.
DOI : 10.1007/978-3-642-33715-4_4

Q. Yang, L. Wang, R. Yang, H. Stewénius, and D. Nistér, Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.3, pp.31492-504, 2009.

M. F. Tous, Segmentation interactive d'images fixes et de séquences vidéo basée sur des hiérarchies de partitions, 2001.

C. L. Zitnick and S. B. Kang, Stereo for Image-Based Rendering using Image Over-Segmentation, International Journal of Computer Vision, vol.22, issue.7, pp.49-65, 2007.
DOI : 10.1007/s11263-006-0018-8