.. Améliorations-de-la-précision, M-estimator SAmple Consensus, p.27

.. Probì-eme-de-l-betasac, estimation robuste des paramètres d'un modèle Nouvelles méthodes d'´ echantillonnage : BetaSAC et OABSAC Sommaire 4, p.51

N. Peter, J. P. Belhumeur, D. J. Hespanha, and . Kriegman, Eigenfaces vs. fisherfaces : Recognition using class specific linear projection, 1997.

P. N. Belhumeur and D. J. Kriegman, What is the set of images of an object under all possible lighting conditions ? In Computer Vision and Pattern Recognition, Proceedings CVPR '96 IEEE Computer Society Conference on, pp.270-277, 1996.

T. Botterill, S. Mills, and R. Green, New Conditional Sampling Strategies for Speeded-Up RANSAC, Procedings of the British Machine Vision Conference 2009, 2009.
DOI : 10.5244/C.23.33

G. Bradski, The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.

M. Brown, G. Hua, and S. Winder, Discriminative learning of local image descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.33, issue.1, pp.43-57, 2011.

E. Bughin and A. Almansa, Planar Patch Detection for Disparity Maps, Proc. 3DPVT, Espace Saint Martin, 2010.

R. Mohr, C. Schmid, and C. Bauckhage, Comparing and evaluating interest points, 1998.
URL : https://hal.archives-ouvertes.fr/inria-00548328

O. Chum-matas-center, J. Chum, and . Matas, Randomized ransac with t d,d test, Image and Vision Computing, pp.448-457, 2002.

S. Choi and J. Kim, Robust regression to varying data distribution and its application to landmark-based localization, 2008 IEEE International Conference on Systems, Man and Cybernetics, pp.3465-3470, 2008.
DOI : 10.1109/ICSMC.2008.4811834

O. Chum and J. Matas, Matching with PROSAC ??? Progressive Sample Consensus, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.220-226, 2005.
DOI : 10.1109/CVPR.2005.221

O. Chum, J. Matas, and J. Kittler, Locally Optimized RANSAC, DAGM-Symposium, pp.236-243, 2003.
DOI : 10.1007/978-3-540-45243-0_31

O. Chum, J. Matas, and . Obdr?álek, Epipolar geometry from three correspondences, Computer Vision ? CVWW'03 : Proceedings of the 8th Computer Vision Winter Workshop, pp.83-88, 2003.

O. Chum, T. Werner, and J. Matas, Two-View Geometry Estimation Unaffected by a Dominant Plane, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.772-779, 2005.
DOI : 10.1109/CVPR.2005.354

L. James and . Crowley, A representation for visual information, Robotics Institute, 1981.

L. James, O. Crowley, and . Riff, Fast computation of scale normalised gaussian receptive fields, Proceedings of the 4th international conference on Scale space methods in computer vision, Scale Space'03, pp.584-598, 2003.

S. Teller, E. Olson, M. Walter, and J. Leonard, Single-cluster spectral graph partitioning for robotics applications, Proceedings of Robotics : Science and Systems (RSS), 2005.

C. L. Feng and Y. S. Hung, A robust method for estimating the fundamental matrix, International Conference on Digital Image Computing, pp.633-642, 2003.

A. Martin, R. C. Fischler, and . Bolles, Random sample consensus : A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.

J. Frahm and M. Pollefeys, RANSAC for (Quasi-)Degenerate data (QDEGSAC), 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), pp.453-460, 2006.
DOI : 10.1109/CVPR.2006.235

L. Goshen and I. Shimshoni, Guided sampling via weak motion models and outlier sample generation for epipolar geometry estimation. pages I, pp.1105-1112, 2005.

T. Tuytelaars, H. Bay, and L. Van-gool, Surf : Speeded up robust features, 1978.

C. Harris and M. Stephens, A Combined Corner and Edge Detector, Procedings of the Alvey Vision Conference 1988, 1988.
DOI : 10.5244/C.2.23

J. Sivic-andrew-zisserman-james-philbin and M. Isard, Descriptor learning for efficient retrieval, ECCV (3), pp.677-691, 2010.

T. Kadir, A. Zisserman, and M. Brady, An Affine Invariant Salient Region Detector, 2004.
DOI : 10.1007/978-3-540-24670-1_18

M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. Der-malsburg et al., Distortion invariant object recognition in the dynamic link architecture. Computers, IEEE Transactions on, vol.42, issue.3, pp.300-311, 1993.

S. Laveau and O. Faugeras, Oriented projective geometry for computer vision, ECCV96, pp.147-156, 1996.
DOI : 10.1007/BFb0015531

T. Lindeberg, Scale-space theory in computer vision, 1994.
DOI : 10.1007/978-1-4757-6465-9

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2003.
DOI : 10.1023/B:VISI.0000029664.99615.94

G. David and . Lowe, Object recognition from local scale-invariant features, Proceedings of the International Conference on Computer Vision, p.1150, 1999.

G. David and . Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, vol.60, issue.2, pp.91-110, 2004.

J. Matas, . Chum, T. Urban, and . Pajdla, Robust wide-baseline stereo from maximally stable extremal regions, ¡ce :title¿British Machine Vision Computing 2002¡/ce :title¿, pp.761-767, 2004.
DOI : 10.1016/j.imavis.2004.02.006

J. Matas and O. Chum, Randomized RANSAC with sequential probability ratio test, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1727-1732, 2005.
DOI : 10.1109/ICCV.2005.198

J. Matas and O. Chum, Randomized RANSAC with sequential probability ratio test, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1727-1732, 2005.
DOI : 10.1109/ICCV.2005.198

K. Mikolajczyk and C. Schmid, Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision, vol.60, issue.1, pp.63-86, 2004.
DOI : 10.1023/B:VISI.0000027790.02288.f2

URL : https://hal.archives-ouvertes.fr/inria-00548554

K. Mikolajczyk and C. Schmid, Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision, vol.60, issue.1, pp.63-86, 2004.
DOI : 10.1023/B:VISI.0000027790.02288.f2

URL : https://hal.archives-ouvertes.fr/inria-00548554

L. Moisan and B. Stival, A Probabilistic Criterion to Detect Rigid Point Matches Between Two Images and Estimate the Fundamental Matrix, International Journal of Computer Vision, vol.57, issue.3, pp.201-218, 2004.
DOI : 10.1023/B:VISI.0000013094.38752.54

URL : https://hal.archives-ouvertes.fr/hal-00171323

H. Murase and S. K. Nayar, Visual learning and recognition of 3-d objects from appearance, International Journal of Computer Vision, vol.37, issue.10, pp.5-24, 1995.
DOI : 10.1007/BF01421486

D. R. Myatt, P. H. Torr, S. J. Nasuto, J. M. Bishop, and R. Craddock, NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag, Procedings of the British Machine Vision Conference 2002, 2002.
DOI : 10.5244/C.16.44

D. R. Myatt, P. H. Torr, S. J. Nasuto, J. M. Bishop, and R. Craddock, NAPSAC: High Noise, High Dimensional Robust Estimation - it's in the Bag, Procedings of the British Machine Vision Conference 2002, 2002.
DOI : 10.5244/C.16.44

K. Ni, H. Jin, and F. Dellaert, Groupsac : Efficient consensus in the presence of groupings, ICCV09, Kyoto ;Japan, 2009.

C. F. Olson, A general method for geometric feature matching and model extraction, International Journal of Computer Vision, vol.45, issue.1, pp.39-54, 2001.
DOI : 10.1023/A:1012317923177

M. Pe?-doch, J. Matas, and O. Chum, Epipolar geometry from two correspondences, ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, pp.215-220, 2006.

M. Pontil and A. Verri, Support vector machines for 3D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.6, pp.637-646, 1998.
DOI : 10.1109/34.683777

J. Rabin, J. Delon, Y. Gousseau, and L. Moisan, MAC-RANSAC : a robust algorithm for the recognition of multiple objects, Proceedings of 3DPTV 2010, p.51, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00466487

E. Rosten and T. Drummond, Machine Learning for High-Speed Corner Detection, European Conference on Computer Vision, pp.430-443, 2006.
DOI : 10.1007/11744023_34

D. Roth, M. Yang, and N. Ahuja, Learning to recognize objects, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.724-731, 2000.
DOI : 10.1109/CVPR.2000.855892

L. Sirovich and M. Kirby, Low-dimensional procedure for the characterization of human faces, Journal of the Optical Society of America A, vol.4, issue.3, pp.519-524, 1987.
DOI : 10.1364/JOSAA.4.000519

S. Edelman and T. Poggio, A network that learns to recognize 3d objects, Nature, 1991.

E. Michael, A. Tipping, J. J. Faul, J. Thomson-avenue, and . Avenue, Fast marginal likelihood maximisation for sparse bayesian models, Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, pp.3-6, 2003.

R. Toldo and A. Fusiello, Real-time incremental j-linkage for robust multiple structures estimation

G. Tolias and Y. Avrithis, Speeded-up, relaxed spatial matching, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126427

URL : http://dspace.lib.ntua.gr/handle/123456789/36348

J. Ben, D. W. Tordoff, and . Murray, Guided-mlesac : Faster image transform estimation by using matching priors, IEEE Trans. Pattern Anal. Mach. Intell, vol.27, issue.10, pp.1523-1535, 2005.

P. Torr and A. Zisserman, MLESAC: A New Robust Estimator with Application to Estimating Image Geometry, Computer Vision and Image Understanding, vol.78, issue.1, pp.138-156, 2000.
DOI : 10.1006/cviu.1999.0832

P. H. Torr and A. Zisserman, MLESAC: A New Robust Estimator with Application to Estimating Image Geometry, Computer Vision and Image Understanding, vol.78, issue.1, pp.138-156, 2000.
DOI : 10.1006/cviu.1999.0832

L. Trujillo and G. Olague, Automated Design of Image Operators that Detect Interest Points, Evolutionary Computation, vol.2003, issue.8, pp.483-507, 2008.
DOI : 10.1109/TPAMI.2006.3

A. Turina, T. Tuytelaars, and L. Van-gool, Efficient grouping under perspective skew, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990483

M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol.10, issue.9, pp.71-86, 1991.
DOI : 10.1007/BF00239352

T. Tuytelaars and L. Van-gool, Matching Widely Separated Views Based on Affine Invariant Regions, International Journal of Computer Vision, vol.59, issue.1, pp.61-85, 2004.
DOI : 10.1023/B:VISI.0000020671.28016.e8

S. Umeyama, Least-squares estimation of transformation parameters between two point patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.13, issue.4, pp.376-380, 1991.

T. Werner and A. Zisserman, Model selection for automated architectural reconstruction from multiple views, British Machine Vision Conference, pp.53-62, 2002.

T. Werner and T. Pajdla, Oriented Matching Constraints, Procedings of the British Machine Vision Conference 2001, 2001.
DOI : 10.5244/C.15.46

C. Schmid, R. Horaud, and Y. Dufournaud, Appariement d'imagesàimages`imagesà deséchellesdeséchelles différentes, 2000.

L. Zelnik-manor and M. Irani, Multiview constraints on homographies, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.2, pp.214-223, 2002.
DOI : 10.1109/34.982901