N. Allezard, M. Dhome, and F. Jurie, Recognition of 3D textured objects by mixing view-based and model-based representations, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.960-963, 2000.
DOI : 10.1109/ICPR.2000.905614
URL : https://hal.archives-ouvertes.fr/inria-00548300

S. Arya, D. Mount, N. Netanyahu, R. Silverman, and A. Wu, An optimal algorithm for approximate nearest neighbor searching fixed dimensions, Journal of the ACM, vol.45, issue.6, pp.891-923, 1998.
DOI : 10.1145/293347.293348

H. Asada and M. Brady, The Curvature Primal Sketch, 2nd Workshop Computer Vision : Representation and Control, 1984.
DOI : 10.1109/TPAMI.1986.4767747

D. Ballard, Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition, vol.13, issue.2, pp.111-122, 1981.
DOI : 10.1016/0031-3203(81)90009-1

J. Belhumeur, P. Hespanha, and D. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.711-720, 1997.
DOI : 10.1109/34.598228

S. Belongie, J. Malik, and J. Puzicha, Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.4, 2001.
DOI : 10.1109/34.993558

P. Besl and R. Jain, Three-dimensional object recognition, ACM Computing Surveys, vol.17, issue.1, pp.75-145, 1985.
DOI : 10.1145/4078.4081

F. Bourgeois and J. Lassalle, An extension of the Munkres algorithm for the assignment problem to rectangular matrices, Communications of the ACM, vol.14, issue.12, pp.802-804, 1971.
DOI : 10.1145/362919.362945

S. Bourgeois, S. Naudet-colette, and M. Dhome, CAD Model Visual Registration from Closed-Contour Neighborhood Descriptors, International Conference on Image Analysis and Recognition, 2006.
DOI : 10.1007/11867661_23

S. Bourgeois, S. Naudet-colette, and M. Dhome, Coarse Visual Registration from Closed-Contour Neighborhood Descriptor, 18th International Conference on Pattern Recognition (ICPR'06), pp.283-287, 2006.
DOI : 10.1109/ICPR.2006.376

S. Bourgeois, S. Naudet-colette, and M. Dhome, Visual Registration of Industrial Metallic Object from Local Contour Descriptors, International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06), pp.273-280, 2006.
DOI : 10.1109/CGIV.2006.91

T. Breuel, Geometric Aspects of Visual Object Recognition Massachusetts Institute of Technology, 1992. [14] R. Brooks. Model-based three-dimensional interpretations of twodimensional images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.5, issue.2, pp.140-150, 1983.

J. Burns, A. Hanson, and E. Riseman, Extracting straight lines, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.4, pp.425-455, 1986.
DOI : 10.1109/tpami.1986.4767808

J. Burns, R. Weiss, and E. Riseman, View variation of point-set and line-segment features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.1, pp.51-68, 1993.
DOI : 10.1109/34.184774

J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.6, p.86

T. Cass, Polynomial-time object recognition in the presence of clutter, occlusion, and uncertainty, DARPA Image Understanding Workshop, 1992.
DOI : 10.1007/3-540-55426-2_96

R. Chin and C. Dyer, Model-based recognition in robot vision, ACM Computing Surveys, vol.18, issue.1, pp.67-108, 1986.
DOI : 10.1145/6462.6464

D. Dementhon, Recognition and tracking of 3d objects by 1d search, DARPA93, pp.653-659, 1993.

D. Dementhon and L. S. Davis, Model-based object pose in 25 lines of code, International Journal of Computer Vision, vol.14, pp.123-141, 1995.

R. Deriche, Using Canny's criteria to derive a recursively implemented optimal edge detector, International Journal of Computer Vision, vol.1, issue.2, pp.167-187, 1987.
DOI : 10.1007/BF00123164

M. Dhome, M. Richetin, J. T. Lapreste, and G. Rives, Determination of the attitude of 3D objects from a single perspective view, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.12, pp.1265-1278, 1989.
DOI : 10.1109/34.41365

S. Dickinson, A. Pentland, and A. Rosenfeld, 3-D shape recovery using distributed aspect matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, pp.174-198, 1992.
DOI : 10.1109/34.121788

T. Drummond and R. Cipolla, Real-time visual tracking of complex structures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.932-946, 2002.
DOI : 10.1109/TPAMI.2002.1017620

M. A. Fischle and R. C. Bolles, Random sample consensus : A paradigm for model fitting with applications to image analysis and automated cartography, Graphics and Image Processing, vol.24, issue.6, pp.381-395, 1981.

M. Fischler and R. 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.
DOI : 10.1145/358669.358692

J. Flusser and T. Suk, Pattern recognition by affine moment invariants, Pattern Recognition, vol.26, issue.1, pp.167-174, 1993.
DOI : 10.1016/0031-3203(93)90098-H

Z. Gigus and J. Malik, Computing the aspect graph for line drawings of polyhedral objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2, pp.113-122, 1990.

W. Grimson, The combinatorics of heuristic search termination for object recognition in cluttered environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.9, pp.920-935, 1991.
DOI : 10.1109/34.93810

W. Grimson, On the sensitivity of the Hough transform for object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.3, pp.255-274
DOI : 10.1109/34.49052

C. Harris, Active Vision, chapter Tracking with rigid models, pp.59-73, 1992.

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.1604

R. Horaud, B. Conio, and O. Leboulleux, An analytic solution for the perspective 4-point problem, Computer Vision, Graphics, and Image Processing, vol.47, issue.1, pp.33-44, 1989.
DOI : 10.1016/0734-189X(89)90052-2
URL : https://hal.archives-ouvertes.fr/inria-00589992

C. Huang, A. Camps, and T. Kanungo, Object recognition using appearancebased parts and relations, International Joint Conferences on Artificial Intelligence, 1997.

X. Huang, B. Wang, and L. Zhang, A new scheme for extraction of affine invariant descriptor and affine motion estimation based on independent component analysis, Pattern Recognition Letters, vol.26, issue.9, pp.1244-1255, 2005.
DOI : 10.1016/j.patrec.2004.11.006

Z. Huang and F. Cohen, Affine-invariant B-spline moments for curve matching, 38] D. Huttenlocher and S. Ulman. Recognizing solid objects by alignment DARPA Image Understanding Workshop, pp.1473-1480, 1988.
DOI : 10.1109/83.536895

D. P. Huttenlocher and S. Ullman, Object recognition using alignment, First International Conference on Computer Vision, 1987.

F. Jurie, Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model, Computer Vision and Image Understanding, vol.73, issue.3, pp.357-373, 1999.
DOI : 10.1006/cviu.1998.0735
URL : https://hal.archives-ouvertes.fr/inria-00548323

T. Kadir, Scale, Saliency and Scene Description, 2002.

M. Khalil and M. Bayoumi, Affine invariants for object recognition using the wavelet transform, Pattern Recognition Letters, vol.23, issue.1-3, pp.57-72, 2002.
DOI : 10.1016/S0167-8655(01)00102-7

Y. Lamdan and H. Wolfson, Geometric Hashing: A General And Efficient Model-based Recognition Scheme, [1988 Proceedings] Second International Conference on Computer Vision, 1998.
DOI : 10.1109/CCV.1988.589995

S. Lazebnik, Local, Semi-Local and Global Models for Texture, Object and Scene Recognition, 2006.

D. Lee and S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol.401, pp.788-7991, 1999.

A. Leonardis and H. Bischof, Robust Recognition Using Eigenimages, Computer Vision and Image Understanding, vol.78, issue.1, pp.99-118, 2000.
DOI : 10.1006/cviu.1999.0830

V. Lepetit and P. Fua, Keypoint recognition using randomized trees, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.9, pp.1465-1479, 2006.
DOI : 10.1109/TPAMI.2006.188
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.183.8088

S. Li, X. Hou, and H. Zhang, Learning spatially localized, parts-based representation, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990477
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.294

T. Lindeberg, Discrete Scale-Space Theory and the Scale-Space Primal Sketch, Royal Institute of Technology, 1991.

D. G. Lowe, Three-dimensional object recognition from single two-dimensional images, Artificial Intelligence, vol.31, issue.3, pp.355-395, 1987.
DOI : 10.1016/0004-3702(87)90070-1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.5388

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.911-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

D. Macrini, A. Shokoufandeh, D. Dickinson, and K. Siddiqi, View-based 3-D object recognition using shock graphs, Object recognition supported by user interaction for service robots, 2002.
DOI : 10.1109/ICPR.2002.1047786
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.3348

J. Matas, O. Chum, M. Urban, and T. Pajdla, Robustwide baseline stereo from maximally stable extremal regions, The British Machine Vision Conference, 2002.
DOI : 10.5244/c.16.36
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.671.8241

A. I. Medalia, Dynamic shape factors of particles, Powder Technology, vol.4, issue.3, pp.117-138, 1970.
DOI : 10.1016/0032-5910(71)80021-9

G. Medioni and A. Francois, 3D structures for generic object recognition, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000.
DOI : 10.1109/ICPR.2000.905270

K. Mikolajczyk, Detection of local features invariant to affine transformations, 2002.

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

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas et al., A Comparison of Affine Region Detectors, International Journal of Computer Vision, vol.65, issue.1-2, 2005.
DOI : 10.1007/s11263-005-3848-x
URL : https://hal.archives-ouvertes.fr/inria-00548528

K. Mikolajczyk, A. Zisserman, and C. Schmid, Shape recognition with edgebased features, The British Machine Vision Conference, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00548226

F. Mokhtarian, Silhouette-based occluded object recognition through curvature scale space, Machine Vision and Applications, pp.87-97, 1997.
DOI : 10.1007/s001380050062

H. Murase and S. 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

R. Nevatia and T. O. Binford, Description and recognition of curved objects???, Artificial Intelligence, vol.8, issue.1, pp.77-98, 1977.
DOI : 10.1016/0004-3702(77)90006-6

S. Obdrzalek and J. Matas, Local Affine Frames for Image Retrieval, International Conference The Challenge of Image and Video Retrieval, pp.318-327, 2002.
DOI : 10.1007/3-540-45479-9_34

S. Obdrzalek and J. Matas, Object Recognition using Local Affine Frames on Distinguished Regions, Procedings of the British Machine Vision Conference 2002, 2002.
DOI : 10.5244/C.16.9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.8408

A. Oirrak, M. Daoudi, and D. Aboutajdine, Affine invariant descriptors using Fourier series, Pattern Recognition Letters, vol.23, issue.10, pp.1109-1118, 1995.
DOI : 10.1016/S0167-8655(02)00027-2

J. Ponce, D. Chelberg, and W. Mann, Invariant properties of straight homogeneous generalized cylinders and their contours, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.9, pp.951-966, 1989.
DOI : 10.1109/34.35498

A. Pope and D. Lowe, Probabilistic models of appearance for 3-d object recognition, International Journal of Computer Vision, vol.40, issue.2, pp.149-167, 2000.
DOI : 10.1023/A:1026502202780

R. Pope and D. Lowe, Learning appearance models for object recognition, International Workshop on Object Representation for Computer Vision, 1996.
DOI : 10.1007/3-540-61750-7_30
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.9439

L. Roberts, Machine perception of three-dimensional solids. Optical and Electro-Optical Information Processing, pp.159-197, 1965.

F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, 3d object modeling and recognition using local affine-invariant image descriptors and multiview spatial constraints, International Journal of Computer Vision, issue.3, p.66, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00548618

C. A. Rothwell, A. Zisserman, D. A. Forsyth, and J. L. Mundy, Planar object recognition using projective shape representation, International Journal of Computer Vision, vol.40, issue.4, pp.57-99, 1995.
DOI : 10.1007/BF01428193
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.6007

R. D. Schiffenbauer, A survey of aspect graphs, 2001.

C. Schmid, Appariement d'images par invariants locaux de niveaux de gris, 1996.
URL : https://hal.archives-ouvertes.fr/tel-00005019

S. Sclaroff and A. Pentland, Modal matching for correspondence and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, issue.6, pp.545-561, 1995.
DOI : 10.1109/34.387502
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.3005

T. Sebastian, P. Klein, and B. Kimia, Recognition of shapes by editing their shock graphs, International Conference on Computer Vision, 2001.
DOI : 10.1109/TPAMI.2004.1273924

A. Selinger, Analysis and Applications of Feature-Based Object Recognition, 2001.

A. Sethi, D. Renaudi, D. Kreigman, and J. Ponce, Curve and Surface Duals and the Recognition of Curved 3D Objects from their Silhouettes, International Journal of Computer Vision, vol.58, issue.1, pp.73-86, 2004.
DOI : 10.1023/B:VISI.0000016148.08046.fc

A. Torralba, W. Murphy, M. Freeman, and . Rubin, Context-based vision system for place and object recognition, Proceedings Ninth IEEE International Conference on Computer Vision, 2003.
DOI : 10.1109/ICCV.2003.1238354
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.3790

Q. Tueng and W. Boles, Wavelet-based affine invariant representation : a tool for recognizing planar objects in 3d space, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, pp.846-857, 1997.

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

L. Vacchetti, V. Lepetit, and P. Fua, Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking, Third IEEE and ACM International Symposium on Mixed and Augmented Reality, 2004.
DOI : 10.1109/ISMAR.2004.24
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.123.4501

S. Zhang, R. Deriche, O. Faugeras, and Q. Luong, A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry, Artificial Intelligence, vol.78, issue.1-2, 1994.
DOI : 10.1016/0004-3702(95)00022-4
URL : https://hal.archives-ouvertes.fr/inria-00074398

A. Zhao and J. Chen, Affine curve moment invariants for shape recognition, Pattern Recognition, vol.30, issue.6, pp.895-901, 1997.
DOI : 10.1016/S0031-3203(96)00126-4

K. Zuiderveld, Graphics Gems IV, chapter Contrast Limited Adaptive Histogram Equalization, pp.474-485, 1994.

3. Méthode and . Fig, C.7 ? Erreur de reprojection du meilleur recalage de l'étalon. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l

3. Méthode and 3. C. Fig, 8 ? Erreur de position 3D des points de surface du meilleur recalage de l'étalon. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l'image de droite représente l

3. Méthode and . Fig, C.9 ? Erreur de reprojection du plus mauvais recalage de l'étalon. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l

3. Méthode and 3. C. Fig, 10 ? Erreur de position 3D des points de surface du plus mauvais recalage de l'étalon. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l'

3. Méthode and 3. C. Fig, 11 ? Erreur de reprojection pour un recalage moyen de l'étalon.Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l

3. Méthode and 3. C. Fig, 12 ? Erreur de position 3D des points de surface pour un recalage moyen de l'étalon. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l'

3. Méthode and 3. C. Fig, 26 ? Erreur de reprojection du meilleur recalage de la culasse. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l

3. Méthode and 3. C. Fig, 28 ? Erreur de reprojection du plus mauvais recalage de la culasse. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l

3. Méthode and 3. C. Fig, 29 ? Erreur de position 3D des points de surface du plus mauvais recalage de la culasse. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l'image de droite représente l

3. Méthode and 3. C. Fig, 30 ? Erreur de reprojection pour un recalage moyen de la culasse. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l

3. Méthode and 3. C. Fig, 31 ? Erreur de position 3D des points de surface pour un recalage moyen de la culasse. Chaque ligne d'images correspond à une méthode différente. L'image de gauche correspond à la reprojection du modèle CAO et l'image de droite représente l

A. C. Résultats-du-recalage-avec, U. Modèle, and . C. Fig, 32 ? Reprojection du modèle de la culasse pour un recalage moyen selon la méthode 2D, p.2