A. Bibliographiehakim and . Farag, CSIFT: A SIFT Descriptor with Color Invariant Characteristics, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1978-1983, 2006.

J. Babaud, A. P. Witkin, M. Baudin, and R. O. Duda, Uniqueness of the Gaussian Kernel for Scale-Space Filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.1, pp.26-33, 1986.
DOI : 10.1109/TPAMI.1986.4767749

M. Bennamoun, Reliability Analysis of the Rank Transform for Stereo Matching, IEEE Transactions on Systems, Man, and Cybernetics, issue.6, pp.31-870, 2001.

A. Bartoli, H. Bay, A. Ess, T. Tuytelaars, and L. V. , Reconstruction et alignement en vision 3D : points, droites, plans et caméras Institut National Polytechnique de Grenoble, septembre 2003 SURF: Speeded Up Robust Features, Thèse de doctorat, pp.346-359, 2006.

P. R. Beaudet, Rotationally invariant image operators, International Conference on Pattern Recognition, pp.579-583, 1978.

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

M. Bleyer, C. Rother, and P. Kohli, Surface Stereo with Soft Segmentation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1570-1577, 2010.

]. S. Chambon and A. Crouzil, Similarity measures for image matching despite occlusions in stereo vision, Pattern Recognition, vol.44, issue.9, pp.2063-2075, 2011.
DOI : 10.1016/j.patcog.2011.02.001

URL : https://hal.archives-ouvertes.fr/hal-00614017

Q. Chen and G. Medioni, A volumetric Stereo Matching Method: Application to Image-Based Modeling, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1029-1034, 1999.

H. Chen and P. Meer, Robust Computer Vision through Kernel Density Estimation, European Conference on Computer Vision, pp.236-250
DOI : 10.1007/3-540-47969-4_16

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.8305

D. Comaniciu and P. Meer, Robust analysis of feature spaces: color image segmentation, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.750-755, 1997.
DOI : 10.1109/CVPR.1997.609410

F. Devernay and O. D. Faugeras, Computing differential properties of 3-D shapes from stereoscopic images without 3-D models, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, 1994.
DOI : 10.1109/CVPR.1994.323831

URL : https://hal.archives-ouvertes.fr/inria-00074369

D. Fofi, T. Sliwa, and Y. Voisin, A comparative survey on invisible structured light, Machine Vision Applications in Industrial Inspection XII, pp.90-98, 2004.
DOI : 10.1117/12.525369

L. R. Ford and D. R. Fulkerson, A simple algorithm for finding maximal network flows and an application to the Hitchcock problem, Journal canadien de math??matiques, vol.9, issue.0, pp.210-218, 1957.
DOI : 10.4153/CJM-1957-024-0

]. W. Freeman-00, E. Freeman, O. T. Pasztor, and . Carmichael, Learning low-level vision, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.25-47, 2000.
DOI : 10.1109/ICCV.1999.790414

G. Gales, A. Crouzil, and S. Chambon, A parallel stereo algorithm that produces dense depth maps and preserves image features Approches simultanées et séquentielles de la mise en correspondance par propagation, Actes du congrès francophone de Vision par Ordinateur, Objectif : Robot Autonome et Système Intelligent Sensoriel, ORASIS, support électronique, Trégastel, juin 2009. [Gales 10a] G. Gales, A. Crouzil et S. Chambon. Complementarity of feature point detectors. Dans International Conference on Computer Vision Theory and Applications, pp.35-49, 1993.

G. Gales, A. Crouzil, and S. Chambon, Détection de points d'intérêt pour la mise en correspondance par propagation. Dans actes du Congrès Reconnaissance des Formes et Intelligence Artificielle, 2010. [Gales 10c] G. Gales, A. Crouzil et S. Chambon. A Region-Based Randomized Voting Scheme for Stereo Matching. Dans International Conference on Visual Computing de Lecture Notes in Computer Science, pp.182-191, 2010.

]. D. Garcia-01 and . Garcia, Mesures de formes et de champs de déplacements tridimensionnels par stéréo-corrélation d'images, Thèse de doctorat, 2001.

S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, issue.6, pp.721-741, 1984.

T. Gevers and A. Smeulders, Color-based object recognition, International Conference on Image Analysis and Processing, pp.319-326, 1997.
DOI : 10.1016/S0031-3203(98)00036-3

V. Gouet, P. Montesinos, R. Deriche, and D. Pelé, Évaluation des détecteurs de points d'intérêt pour la couleur, actes du Congrès Reconnaissance des Formes et Intelligence Artificielle, pp.257-266, 2000.

A. W. Gruen, Adaptative least squares correlation: a powerfull image matching technique, pp.175-187, 1985.

R. Hartley and A. Zisserman, Multiple view geometry, Alvey Vision Conference, pp.147-151, 1988.

J. Harvent, H. Hirschmüller, P. Innocent, and J. Garibaldi, Mesures de formes par corrélation multi-images : application à l'inspection de pièces aéronautiques à l'aide d'un système multi-caméras Real-Time Correlation-Based Stereo Vision with Reduced Border Errors, Thèse de doctoratHirschmüller 07] H. Hirschmüller et D. Scharstein. Evaluation of Cost Functions for Stereo Matching. Dans IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.229-246, 2002.

]. J. Holland-75 and . Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence, 1975.

]. L. Hong and G. Chen, Segment-based stereo matching using graph cuts, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.74-81, 2004.
DOI : 10.1109/CVPR.2004.1315016

R. Horaud and O. Monga, Vision par ordinateur, outils fondamentaux. Hermès, 1993.
URL : https://hal.archives-ouvertes.fr/inria-00590049

]. P. Huber-81, F. Huber, C. Jurie, and . Schmid, Robust statistics Scale-invariant shape features for recognition of object categories, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.90-96, 1981.

T. Kadir, A. Zisserman, and M. Brady, An Affine Invariant Salient Region Detector, European Conference on Computer Vision, pp.228-241, 2004.
DOI : 10.1007/978-3-540-24670-1_18

T. Kanade and M. Okutomi, A stereo matching algorithm with an adaptive window: theory and experiment, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.16, issue.9, pp.920-932, 1994.
DOI : 10.1109/34.310690

]. J. Kannala and S. S. Brandt, Quasi-Dense Wide Baseline Matching Using Match Propagation, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383247

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.8158

]. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

]. S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies, Journal of Statistical Physics, vol.21, issue.5-6, pp.5-6, 1984.
DOI : 10.1007/BF01009452

L. Kitchen and A. Rosenfeld, Gray-level corner detection, Pattern Recognition Letters, vol.1, issue.2, pp.95-102, 1982.
DOI : 10.1016/0167-8655(82)90020-4

A. Klaus, M. Sormann, and K. Karner, Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure, 18th International Conference on Pattern Recognition (ICPR'06), pp.15-18, 2006.
DOI : 10.1109/ICPR.2006.1033

J. Kostková and R. ?ára, Disparity component matching revisited, 2002.

M. Lhuillier and L. Quan, Match propagation for image-based modeling and rendering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.8, pp.1140-1146, 2002.
DOI : 10.1109/TPAMI.2002.1023810

URL : https://hal.archives-ouvertes.fr/hal-00118524

]. S. Li, Markov random field models in computer vision, European Conference on Computer Vision, pp.361-370, 1994.
DOI : 10.1007/BFb0028368

M. H. Lin and C. Tomasi, Surfaces with occlusions from layered stereo, Lindeberg 90] T. Lindeberg. Scale-Space for Discret Signals, pp.1073-1078, 1990.
DOI : 10.1109/TPAMI.2004.54

]. J. Lotti and G. Giraudon, Correlation algorithm with adaptive window for aerial image in stereo vision, European Symposium on Satellite Remote Sensing, pp.2315-2325, 1994.

]. D. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.4065

M. , W. J. Liu, K. Mikolajczyk, and C. Schmid, Progressive Matching Based on Segmentation for 3D reconstruction Indexing based on scale invariant interest points, International Conference on Computer and Information Technology IEEE International Conference on Computer Vision, pp.575-579, 2001.

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1615-1630, 2005.
DOI : 10.1109/TPAMI.2005.188

URL : https://hal.archives-ouvertes.fr/inria-00548227

P. Montesinos, V. Gouet, and R. Deriche, Differential invariants for color images, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.838-840, 1998.
DOI : 10.1109/ICPR.1998.711280

H. P. Moravec, Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover, 1980.

J. A. Noble, Finding corners, Image and Vision Computing, vol.6, issue.2, pp.121-128, 1988.
DOI : 10.1016/0262-8856(88)90007-8

G. P. Otto and T. K. Chau, ???Region-growing??? algorithm for matching of terrain images, Image and Vision Computing, vol.7, issue.2, pp.83-94, 1989.
DOI : 10.1016/0262-8856(89)90001-2

P. Parisot, J. Park, W. Kim, and K. M. Lee, Suivi d'objets dans les séquences d'images de scènes déformables : de l'importance des points d'intérêt et du maillage 2D, Thèse de doctorat Stereo Matching Using Population-Based MCMC. Dans Asian Conference on Computer Vision, pp.560-569, 2007.

J. Pearl, Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach, National Conference on Artificial Intelligence, pp.133-136, 1982.

M. Devy, E. H. Bouyakhf, and T. Drummond, Scale and Rotation Invariant Local Feature Using Harris Laplace Detector in Color Textured Images Sciences of Electronics, Technologies of Information and Telecommunication , support électronique, mars Machine learning for high-speed corner detection, International Conference European Conference on Computer Vision, pp.430-443, 2006.

P. J. Rousseeuw and C. Croux, L 1 -Statistical Analysis and Related Methods Dodge, éditeur, Explicit Scale Estimators with High Breakdown Point, pp.77-92, 1992.

S. Roy, Stereo Without Epipolar Lines: A Maximum-Flow Formulation, International Journal of Computer Vision, vol.34, issue.2/3, pp.147-161, 1999.
DOI : 10.1023/A:1008192004934

R. Szeliski, A Taxomomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, International Journal of Computer Vision, vol.47, issue.1, pp.7-42, 2002.

]. D. Scharstein and R. Szeliski, High-accuracy stereo depth maps using structured light, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.195-202, 2003.
DOI : 10.1109/CVPR.2003.1211354

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.6857

C. Pal, On Learning Conditional Random Fields for Stereo, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1007/s11263-010-0385-z

C. Schmid, R. Mohr, and C. Bauckhage, Evaluation of interest point detectors, International Journal of Computer Vision, vol.37, issue.2, pp.151-172, 2000.
DOI : 10.1023/A:1008199403446

URL : https://hal.archives-ouvertes.fr/inria-00548302

J. Shi and C. Tomasi, Good features to track, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.593-600, 1994.

S. M. Smith and J. M. Brady, SUSAN ? A New Approach to Low Level Image Processing, International Journal of Computer Vision, vol.23, issue.1, pp.45-78, 1997.
DOI : 10.1023/A:1007963824710

J. Sun, H. Shum, and N. Zheng, Stereo Matching Using Belief Propagation, European Conference on Computer Vision, pp.450-452, 2002.
DOI : 10.1007/3-540-47967-8_34

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.2039

J. Sun, S. B. Kang, and H. Y. Shum, Symmetric stereo matching for occlusion handling, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.399-406, 2005.

F. Sur, N. Noury, and M. O. Berger, Determining point correspondences between two views under geometric constraint and photometric consistency, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00471874

R. Szeliski, Bayesian Modeling of Uncertainty in Low-Level Vision, International Journal of Computer Vision, vol.5, issue.3, pp.271-302, 1990.
DOI : 10.1007/978-1-4613-1637-4

R. Szeliski, Computer vision: Algorithms and applications, 2010.
DOI : 10.1007/978-1-84882-935-0

Y. Taguchi, B. Wiburn, and C. L. Zitnick, Stereo reconstruction with mixed pixels using adaptive over-segmentation, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587691

T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey. Foundations and Trends R in Computer Graphics and Vision, pp.177-280, 2008.

]. O. Veksler, Efficient Graph-Based Energy Minimization Methods in Computer Vision, 1999.

E. Vincent and R. Laganière, Matching Feature Points in Stereo Pairs: A Comparative Study of Some Matching Strategies. Machine Graphics & Vision, pp.237-259, 2001.

]. Z. Wang and Z. G. Zheng, A region based stereo matching algorithm using cooperative optimization, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.

]. L. Xu and J. Jia, Stereo Matching: An Outlier Confidence Approach, European Conference on Computer Vision, pp.775-787, 2008.
DOI : 10.1007/978-3-540-88693-8_57

Q. Yang, L. Wang, R. Yang, H. Stewénius, and D. Nistér, Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.3, pp.492-504, 2009.
DOI : 10.1109/TPAMI.2008.99

]. K. Yoon and I. S. Kweon, Locally Adaptive Support-Weight Approach for Visual Correspondence Search, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.924-931, 2005.

R. Zabih and J. Woodfill, Non-parametric local transforms for computing visual correspondence, European Conference on Computer Vision, pp.151-158, 1994.
DOI : 10.1007/BFb0028345

]. J. Zhou and J. Shi, A robust algorithm for feature point matching, Computers & Graphics, vol.26, issue.3, pp.429-436, 2002.
DOI : 10.1016/S0097-8493(02)00086-9

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