M. Rodriguez, G. Facciolo, R. Grompone-von-gioi, P. Musé, and J. Delon, Robust estimation of local affine maps and its applications to image matching, WACV, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02156259

M. Rodriguez, G. Facciolo, R. Grompone-von-gioi, P. Musé, J. Delon et al., CNN-assisted coverings in the space of tilts: best affine invariant performances with the speed of CNNs, ICIP, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02494121

, Les codes sources ont été publiés sur github

, Le cadre de test, les figures et l'optimiseur de recouvrements apparaissant dans

, Toutes les méthodes IMAS présentées dans

, La méthode SIFT-AID ainsi que tous les résultats apparaissant dans

, Optimal Affine-RootSIFT results for the 'adam' image pair

, Results of Algorithm 11 for query and target images from the 'adam' image pair. (c) Adaptive-ARootSIFT results for the 'adam' image pair

, Results of Algorithm 12 for query and target images from the 'adam' image pair. (e) Greedy-ARootSIFT results for the 'adam' image pair

, Results of Algorithm 11 for query and target images from the 'notredame' image pair. (c) Adaptive-ARootSIFT results for the 'notredame' image pair

, Results of Algorithm 12 for query and target images from the 'notredame' image pair. (e) Greedy-ARootSIFT results for the 'notredame' image pair

A. Agarwala, M. Agrawala, D. Cohen, R. Salesin, and . Szeliski, Photographing long scenes with multi-viewpoint panoramas, International Conference on Computer Graphics and Interactive Techniques, pp.853-861, 2006.

P. Fernández-alcantarilla, A. Bartoli, and A. J. Davison, KAZE features, Lecture Notes in Computer Science, volume, vol.7577, pp.214-227, 2012.

Y. Afs-+-11]-sameer-agarwal, N. Furukawa, I. Snavely, B. Simon, . Curless et al., Building rome in a day, Communications of the ACM, vol.54, issue.10, pp.105-112, 2011.

P. Fernández-alcantarilla, J. Nuevo, and A. Bartoli, Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces, British Machine Vision Conference, vol.11, pp.13-14, 2013.

N. A-p-ashbrook, P. Thacker, C. Rockett, and . Brown, Robust recognition of scaled shapes using pairwise geometric histograms, BMVC, pp.503-512, 1995.

R. Arandjelovic and A. Zisserman, Three things everyone should know to improve object retrieval, CVPR, pp.2911-2918, 2012.

A. Baumberg, Reliable feature matching across widely separated views, CVPR, vol.1, pp.774-781, 2000.

D. Barath and L. Hajder, Novel ways to estimate homography from local affine transformations, 2016.

M. Brown and . Lowe, Recognising panoramas, Proc. the 9th Int. Conf. Computer Vision, October, pp.1218-1225, 2003.

J. Blom, Topological and Geometrical Aspects of Image Structure. University of Utrecht, 1992.

S. Belongie, J. Malik, and J. Puzicha, Shape matching and object recognition using shape contexts, TPAMI, vol.24, issue.4, pp.509-522, 2002.

M. Brown and S. Süsstrunk, Multi-spectral SIFT for scene category recognition, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.177-184, 2011.

M. Bennewitz, C. Stachniss, W. Burgard, and S. Behnke, Metric Localization with Scale-Invariant Visual Features Using a Single Perspective Camera, European Robotics Symposium, 2006.

H. Bay, T. Tuytelaars, and L. Van-gool, Surf: Speeded up robust features. ECCV, vol.1, pp.404-417, 2006.

E. Y. Chang, ;. Cao, J. Lisani, J. Morel, P. Musé et al., EXTENT: fusing context, content, and semantic ontology for photo annotation, Proceedings of the 2nd international workshop on Computer vision meets databases, pp.5-11, 2005.

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, BRIEF: Binary robust independent elementary features, Lecture Notes in Computer Science, volume, vol.6314, pp.778-792, 2010.

O. Chum, J. Matas, and J. Kittler, Locally Optimized RANSAC. Proceedings of the DAGM, vol.2781, pp.236-243, 2003.

D. Cozzolino, G. Poggi, and L. Verdoliva, Efficient dense-field copy-move forgery detection, IEEE Transactions on Information Forensics and Security, vol.10, issue.11, pp.2284-2297, 2015.

A. Desolneux, L. Moisan, and J. Morel, From Gestalt Theory to Image Analysis, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00259077

P. Doubek, J. Matas, M. Perdoch, and O. Chum, Image matching and retrieval by repetitive patterns, 20th International Conference on Pattern Recognition (ICPR), pp.3195-3198, 2010.

D. Detone, T. Malisiewicz, and A. Rabinovich, Deep image homography estimation, 2016.

O. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint, 1993.

M. A. Fischler and R. C. 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.

. Q-fan, . Barnard, . Amir, M. Efrat, and . Lin, Matching slides to presentation videos using SIFT and scene background matching, Proceedings of the 8th ACM international workshop on Multimedia information retrieval, pp.239-248, 2006.

L. Février, A wide-baseline matching library for Zeno, 2007.

E. Farhan and R. Hagege, Geometric expansion for local feature analysis and matching, SIAM Journal on Imaging Sciences, vol.8, issue.4, pp.2771-2813, 2015.

E. Farhan, E. Meir, and R. Hagege, Local Region Expansion: a Method for Analyzing and Refining Image Matches, Image Processing On Line, vol.7, pp.386-398, 2017.

J. Jun, R. Foo, and . Sinha, Pruning SIFT for scalable near-duplicate image matching, ADC '07: Proceedings of the eighteenth conference on Australasian database, pp.63-71, 2007.

G. Fritz, M. Seifert, L. Kumar, and . Paletta, Building detection from mobile imagery using informative SIFT descriptors, Lecture Notes in Computer Science, pp.629-638

A. Gordon, G. Glazko, X. Qiu, and A. Yakovlev, Control of the mean number of false discoveries, bonferroni and stability of multiple testing, Ann. Appl. Stat, vol.1, pp.179-190, 2007.

I. Gordon and D. Lowe, What and Where: 3D Object Recognition with Accurate Pose, Lecture Notes in Computer Science, vol.4170, p.67, 2006.

Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.12, pp.2916-2929, 2013.

R. Grompone-von-gioi and V. P?tr?ucean, A contrario patch matching, with an application to keypoint matches validation, ICIP, pp.946-950, 2015.

A. Geiger, J. Ziegler, and C. Stiller, Stereoscan: Dense 3d reconstruction in real-time, Intelligent Vehicles Symposium (IV), 2011.

, IEEE, pp.963-968, 2011.

J. Hare and P. Lewis, Salient regions for query by image content. Image and Video Retrieval, Third International Conference, CIVR, pp.317-325, 2004.

C. Harris and M. Stephens, Alvey Vision Conference, vol.15, p.50, 1988.

D. C. Hauagge and N. Snavely, Image matching using local symmetry features, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.206-213, 2012.

R. Hartley and A. Zisserman, Multiple view geometry in computer vision, 2003.

T. Iijima, Basic equation of figure and and observational transformation, Computers, Controls, vol.2, issue.4, pp.70-77, 1971.

M. Karpushin, Local features for RGBD image matching under viewpoint changes, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01483314

S. Korman, D. Reichman, G. Tsur, and S. Avidan, Fast-Match: Fast Affine Template Matching, Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pp.1940-1947, 2013.

Y. Ke and . Sukthankar, PCA-SIFT: A more distinctive representation for local image descriptors, CVPR, vol.2, pp.506-513, 2004.

A. Kelman, M. Sofka, and C. V. Stewart, Keypoint descriptors for matching across multiple image modalities and non-linear intensity variations, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.

T. Kadir, M. Zisserman, and . Brady, An Affine Invariant Salient Region Detector, ECCV, pp.228-241, 2004.

H. Lejsek, H. Fridrik, . Ásmundsson, L. Björn-thór-jónsson, and . Amsaleg, Scalability of local image descriptors: a comparative study, MULTIMEDIA '06: Proceedings of the 14th annual ACM international conference on Multimedia, pp.589-598, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00175234

W. B-n-lee, E. Chen, and . Chang, Fotofiti: web service for photo management, Proceedings of the 14th annual ACM international conference on Multimedia, pp.485-486, 2006.

S. Leutenegger, M. Chli, and R. Y. Siegwart, BRISK: Binary Robust invariant scalable keypoints, Proceedings of the IEEE International Conference on Computer Vision, pp.2548-2555, 2011.

G. Loy and J. Eklundh, Detecting symmetry and symmetric constellations of features, Proceedings of ECCV, vol.2, pp.508-521, 2006.

T. Lindeberg and J. Garding, Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure, ECCV, pp.389-400, 1994.

G. Levi and T. Hassner, LATCH: Learned arrangements of three patch codes, 2016 IEEE Winter Conference on Applications of Computer Vision, 2016.

T. Lindeberg, Scale-Space Theory in Computer Vision. Royal Institute of Technology, 1993.

T. Lindeberg, Direct estimation of affine image deformations using visual front-end operations with automatic scale selection, Computer Vision, 1995. Proceedings., Fifth International Conference on, pp.134-141, 1995.

T. Lindeberg, Generalized gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space, Journal of Mathematical Imaging and Vision, vol.40, issue.1, pp.36-81, 2011.

T. Lindeberg, Invariance of visual operations at the level of receptive fields, BMC Neuroscience, vol.14, issue.1, p.242, 2013.

T. Lindeberg, ;. Lin, M. Maire, S. Belongie, J. Hays et al., Scale selection properties of generalized scale-space interest point detectors, ECCV, vol.46, pp.740-755, 2013.

D. Lowe, Perceptual Organization and Visual Recognition, 1985.

D. Lowe, Distinctive image features from scale-invariant keypoints, IJCV, vol.60, issue.2, pp.91-110, 2004.

C. Liu, J. Yuen, and A. Torralba, Sift flow: Dense correspondence across scenes and its applications, IEEE transactions on pattern analysis and machine intelligence, vol.33, pp.978-994, 2011.

J. Matas, O. Chum, M. Urban, T. Pajdla-;-elmar, G. D. Mair et al., Adaptive and generic corner detection based on the accelerated segment test, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 6312 LNCS, vol.22, pp.183-196, 2004.

J. L. Marichal and M. J. Mossinghoff, Slices, slabs, and sections of the unit hypercube, Online Journal of Analytic Combinatorics, 2006.

A. Murarka, B. Modayil, and . Kuipers, Building Local Safety Maps for a Wheelchair Robot using Vision and Lasers, Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision, 2006.

L. Moisan, P. Moulon, and P. Monasse, Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of, Outliers. IPOL, vol.2, pp.56-73, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00711852

L. Moisan, P. Moulon, and P. Monasse, Fundamental Matrix of a Stereo Pair, with A Contrario Elimination of Outliers, IPOL, vol.6, pp.89-113, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01386806

D. Mishkin, J. Matas, and M. Perdoch, MODS: Fast and robust method for two-view matching, vol.141, pp.81-93, 2015.

A. Mishchuk, D. Mishkin, F. Radenovic, and J. Matas, Working hard to know your neighbor's margins: Local descriptor learning loss, Advances in Neural Information Processing Systems, pp.4826-4837, 2017.

P. Moreels and P. Perona, Common-frame model for object recognition, Neural Information Processing Systems, 2004.

P. Moreels and P. Perona, Evaluation of features detectors and descriptors based on 3d objects, ICCV, pp.800-807, 2005.

P. Moreels and P. Perona, Evaluation of Features Detectors and Descriptors based on 3D Objects, IJCV, vol.73, issue.3, pp.263-284, 2007.

D. Mishkin, F. Radenovic, and J. Matas, Repeatability is not enough: Learning affine regions via discriminability, Proceedings of the European Conference on Computer Vision (ECCV), pp.284-300, 2018.

K. Mikolajczyk and C. Schmid, Indexing based on scale invariant interest points. ICCV, vol.1, pp.525-531, 2001.
URL : https://hal.archives-ouvertes.fr/inria-00548276

K. Mikolajczyk and C. Schmid, An affine invariant interest point detector. ECCV, vol.1, pp.128-142, 2002.

K. Mikolajczyk and C. Schmid, , vol.60, pp.63-86, 2004.

K. Mikolajczyk and C. Schmid, A Performance Evaluation of Local Descriptors, IEEE Trans. PAMI, pp.1615-1630, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548227

P. Musé, F. Sur, F. Cao, Y. Gousseau, and J. Morel, An A Contrario Decision Method for Shape Element Recognition, IJCV, vol.69, issue.3, pp.295-315, 2006.

P. Musé, F. Sur, F. Cao, and Y. Gousseau, Unsupervised thresholds for shape matching, ICIP, 2003.

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas et al., A Comparison of Affine Region Detectors, IJCV, vol.65, issue.1, pp.43-72, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548528

J. Yu, ;. , and C. Cachan, On the consistency of the SIFT Method, Inverse Problems and Imaging (IPI), 2008.

J. Morel and G. Yu, ASIFT: A new framework for fully affine invariant image comparison, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.438-469, 2009.

J. M. Morel and . Yu, Is SIFT scale invariant?, Inv. Problems and Imaging, vol.5, issue.1, pp.115-136, 2011.

D. Nister, H. Stewenius-;-a-negre, H. Tran, . Gourier, . Hall et al., Comparative study of People Detection in Surveillance Scenes. Structural, Syntactic and Statistical Pattern Recognition, Proceedings Lecture Notes in Computer Science, vol.4109, pp.100-108, 2006.

E. Oyallon and J. Rabin, An Analysis of the SURF Method, Image Processing On Line, vol.5, pp.176-218, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01657161

D. Pritchard and W. Heidrich, Cloth Motion Capture, Computer Graphics Forum, vol.22, issue.3, pp.263-271, 2003.

Y. Pang, W. Li, Y. Yuan, and J. Pan, Fully affine invariant SURF for image matching, Neurocomputing, vol.85, pp.6-10, 2012.

V. P?tr?ucean, Detection and Identification of Elliptical Structure Arrangements in Images: Theory and Algorithms, 2012.

I. Rocco, R. Arandjelovic, and J. Sivic, Convolutional neural network architecture for geometric matching, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01859616

C. Raposo, J. P. Barreto, ;. R. Raguram, O. Chum, M. Pollefeys et al., Theory and practice of structurefrom-motion using affine correspondences, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.35, pp.2022-2038, 2013.

J. Rabin, J. Delon, and Y. Gousseau, A statistical approach to the matching of local features, SIIMS, vol.2, issue.3, pp.931-958, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00168285

M. Rodriguez, J. Delon, and J. Morel, Covering the space of tilts. application to affine invariant image comparison, SIIMS, vol.11, issue.2, pp.1230-1267, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01589522

M. Rodriguez, J. Delon, and J. , Morel. Fast affine invariant image matching. IPOL, vol.8, pp.251-281, 2018.

J. Ruiz-del-solar, C. Loncomilla, and . Devia, A New Approach for Fingerprint Verification Based on Wide Baseline Matching Using Local Interest Points and Descriptors, Lecture Notes in Computer Science, vol.4872, p.586, 2007.

]. M. +-19, G. Rodriguez, R. Facciolo, P. Grompone-von-gioi, J. Musé et al., Sift-aid: boosting sift with an affine invariant descriptor based on convolutional neural networks, In ICIP, 2019.

M. Rodriguez, G. Facciolo, R. Grompone-von-gioi, P. Musé, and J. Delon, Robust estimation of local affine maps and its applications to image matching, WACV, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02156259

M. Rais, G. Facciolo, E. Meinhardt-llopis, J. Morel, and A. Buades, Accurate motion estimation through random sample aggregated consensus, 2017.

G. Rfvg-+-20]-mariano-rodríguez, R. Facciolo, P. Grompone-von-gioi, J. Musé, J. Delon et al., Cnn-assisted coverings in the space of tilts: best affine invariant performances with the speed of cnns, ICIP, 2020.

M. Rodriguez and R. Grompone-von-gioi, Affine invariant image comparison under repetitive structures, ICIP, pp.1203-1207, 2018.

I. Rey-otero and M. Delbracio, Anatomy of the SIFT method, IPOL, vol.4, pp.370-396, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01226489

I. Rey-otero, M. Delbracio, and J. Morel, Comparing feature detectors: A bias in the repeatability criteria, 2015 IEEE International Conference on Image Processing (ICIP), pp.3024-3028, 2015.

F. Riggi and . Toews, Fundamental Matrix Estimation via TIP-Transfer of Invariant Parameters, Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06, vol.02, pp.21-24, 2006.

P. Scovanner, A. Saad, and M. Shah, A 3-dimensional SIFT descriptor and its application to action recognition, MULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia, pp.357-360, 2007.

E. Shechtman and M. Irani, Matching local self-similarities across images and videos, CVPR, pp.1-8, 2007.

S. Se, J. Lowe, K. Snoek, . Sande, B. Od-rooij et al., Vision-based mobile robot localization and mapping using scale-invariant features. Robotics and Automation, ICRA. IEEE International Conference on, vol.2, 2001.

N. Snavely, R. Seitz, and . Szeliski, Photo tourism: exploring photo collections in 3D, ACM Transactions on Graphics (TOG), vol.25, issue.3, pp.835-846, 2006.

F. Schaffalitzky, ;. Zisserman, A. Sivic, and . Zisserman, Multi-view matching for unordered image sets, or How do I organize my holiday snaps? ECCV, iccv, vol.1, pp.1470-1477, 2002.

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, 2014.

T. Tuytelaars and L. Van-gool, Wide baseline stereo matching based on local, affinely invariant regions, BMVC, pp.412-425, 2000.

T. Tuytelaars and L. Van-gool, Matching Widely Separated Views Based on Affine Invariant Regions, IJCV, vol.59, issue.1, pp.61-85, 2004.

T. Tuytelaars, L. Van-gool, and O. , Content-based image retrieval based on local affinely invariant regions. Int. Conf. on Visual Information Systems, pp.493-500, 1999.

K. Van-de-sande, T. Gevers, and C. Snoek, Evaluating color descriptors for object and scene recognition, IEEE transactions on pattern analysis and machine intelligence, vol.32, pp.1582-1596, 2010.

C. Valgren, SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments, Robotics and Autonomous Systems, vol.58, issue.2, pp.149-156, 2010.

L. Van-gool, T. Moons, and D. Ungureanu, Affine/Photometric Invariants for Planar Intensity Patterns, Proceedings of the 4th European Conference on Computer Vision-Volume I-Volume I, pp.642-651, 1996.

M. Vergauwen and L. Van-gool, Web-based 3D Reconstruction Service. Machine Vision and Applications, vol.17, pp.411-426, 2005.

M. Veloso, P. Von-hundelshausen, and . Rybski, Learning visual object definitions by observing human activities, Proc. of the IEEE-RAS Int. Conf. on Humanoid Robots, pp.148-153, 2005.

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, Deepflow: Large displacement optical flow with deep matching, Proceedings of the IEEE International Conference on Computer Vision, pp.1385-1392, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00873592

A. P. Witkin and J. M. Tenenbaum, On the role of structure in vision, Human and Machine Vision, pp.481-543, 1983.

K. Yanai, Image collector III: a web image-gathering system with bag-ofkeypoints, Proc. of the 16th Int. Conf. on World Wide Web, pp.1295-1296, 2007.

J. Yao and W. Cham, Robust multi-view feature matching from multiple unordered views, Pattern Recognition, vol.40, issue.11, pp.3081-3099, 2007.

G. Yu and J. Morel, ASIFT: An Algorithm for Fully Affine Invariant, Comparison. IPOL, vol.1, pp.1-28, 2011.

G. Yang, C. Stewart, M. Sofka, and C. Tsai, Alignment of challenging image pairs: Refinement and region growing starting from a single keypoint correspondence, IEEE Trans. Pattern Anal. Machine Intell, 2007.

Y. Zheng and D. Doermann, Robust point matching for nonrigid shapes by preserving local neighborhood structures, IEEE transactions on pattern analysis and machine intelligence, vol.28, pp.643-649, 2006.

S. Zagoruyko and N. Komodakis, Learning to compare image patches via convolutional neural networks, CVPR, pp.4353-4361, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01246261

J. Zbontar and Y. Lecun, Stereo matching by training a convolutional neural network to compare image patches, JMLR, vol.17, issue.2, pp.1-32, 2016.

C. L. Zitnick and K. Ramnath, Edge foci interest points, 2011 International Conference on Computer Vision, pp.359-366, 2011.

H. Zhou, Y. Yuan, and C. Shi, Object tracking using SIFT features and mean shift, Computer vision and image understanding, vol.113, issue.3, pp.345-352, 2009.