N. Aronszajn and K. Smith, Functional spaces and functional completion. Annales de l'institut Fourier, pp.125-185, 1956.
DOI : 10.5802/aif.63

URL : http://archive.numdam.org/article/AIF_1956__6__125_0.pdf

F. R. Bach, G. R. Lanckriet, and M. I. Jordan, Multiple kernel learning, conic duality, and the SMO algorithm, Twenty-first international conference on Machine learning , ICML '04, p.6, 2004.
DOI : 10.1145/1015330.1015424

S. Barnard and M. Fischler, Computational Stereo, ACM Computing Surveys, vol.14, issue.4, pp.553-572, 1982.
DOI : 10.1145/356893.356896

H. G. Barrow, J. M. Tenenbaum, R. C. Boles, and H. C. Wolf, Parametric correspondence and chamfer matching : Two new techniques for image matching, IJCAI, pp.659-663, 1977.

C. Berge, Théorie des Graphes et ses Applications, 1958.

M. Bertozzi, A. Broggi, G. Conte, and A. Fascioli, Stereo vision system performance analysis, Enabling Technologies for the PRASSI Autonomous Robot, pp.68-73, 2002.

M. Bertozzi, A. Broggi, M. D. Rose, and A. Lasagni, Infrared Stereo Vision-based Human Shape Detection, Procs. IEEE Intelligent Vehicles Symposium 2005, pp.23-28, 2005.
DOI : 10.1109/ivs.2005.1505072

M. Bertozzi, A. Broggi, A. Fascioli, T. Graf, and M. Meinecke, Pedestrian Detection for Driver Assistance Using Multiresolution Infrared Vision, IEEE Transactions on Vehicular Technology, vol.53, issue.6, pp.1666-1678, 2004.
DOI : 10.1109/TVT.2004.834878

H. Blum, A transformation for extracting new descriptors of shape, Proceedings of Models for the Perception of Speech and Visual Form, pp.362-380, 1967.

S. Boughorbel, J. Tarel, and N. Boujemaa, Generalized histogram intersection kernel for image recognition, IEEE International Conference on Image Processing 2005, pp.161-164, 2005.
DOI : 10.1109/ICIP.2005.1530353

A. Broggi, M. Bertozzi, R. Chapuis, F. C. , A. Fascioli et al., Pedestrian localization and tracking system with kalman filtering, Procs. IEEE Intelligent Vehicles Symposium, pp.584-589, 2004.

A. Broggi, M. Bertozzi, A. Fascioli, and G. Conte, Automatic Vehicle Guidance : the Experience of the ARGO Vehicle, World Scientific, 1999.
DOI : 10.1142/3986

H. Bunke and K. Shearer, A graph distance metric based on the maximal common subgraph, Pattern Recognition Letters, vol.19, issue.3-4, pp.255-259, 1998.
DOI : 10.1016/S0167-8655(97)00179-7

O. Chapelle, P. Haffner, and V. Vapnik, Svms for histogram based image classification, IEEE Transactions on Neural Networks, vol.9, 1999.

J. Cocquerez and S. Philipp, Analyse d'images : filtrage et segmentation, 1995.
URL : https://hal.archives-ouvertes.fr/hal-00706168

D. Conte, P. Foggia, J. Jolion, and M. Vento, Un algorithme multirésolution pour la gestion des occlusions basé sur les pyramides de graphes, Compression et Représentation des Signaux Audiovisuels, 2005.

C. Cortes, P. Haffner, and M. Mohri, Rational kernels : Theory and algorithms, Journal of Machine Learning Research, vol.5, pp.1035-1062, 2004.

N. Cristianini and J. Shawe-taylor, Introduction to Support Vector Machines, 2000.

M. Christopher, B. B. Cyr, and . Kimia, 3d object recognition using shape similarity-based aspect graph, ICCV, pp.254-261, 2001.

C. Dai, Y. Zheng, and X. Li, Layered representation for pedestrian detection and tracking in infrared imagery, CVPR '05 : Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) -Workshops, p.13, 2005.

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, European Conference on Computer Vision, Austria, pp.428-441, 2006.
DOI : 10.1023/A:1008162616689

URL : https://hal.archives-ouvertes.fr/inria-00548587

J. Giebel, D. Gavrila, and S. Munder, Vision-based pedestrian detection : the protector+ system, Proceedings of the IEEE Intelligent Vehicles Symposium, pp.13-18, 2004.

E. Dijkstra, A note on two problems in connexion with graphs, Numerische Mathematik, vol.4, issue.1, pp.269-271, 1959.
DOI : 10.1007/BF01386390

P. Dimitrov, C. Phillips, and K. Siddiqi, Robust and efficient skeletal graphs, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.417-423, 2000.
DOI : 10.1109/CVPR.2000.855849

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.2500

G. Dorkó and C. Schmid, Maximally Stable Local Description for Scale Selection, European Conference on Computer Vision, 2006.
DOI : 10.1007/11744085_39

R. Duda, P. Hart, and D. Stork, Pattern Classification, 2000.

J. Eichhorn and O. Chapelle, Object categorization with svm : kernels for local features, 2004.

S. Hadi-elzein, P. Lakshmanan, and . Watta, A motion and shape-based pedestrian detection algorithm, Intelligent Vehicles Symposium, pp.500-504, 2003.

Y. Fang, K. Yamadac, Y. Ninomiya, B. Horn, and I. Masaki, Comparison between infrared-image-based and visible-image-based approaches for pedestrian detection, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), pp.505-510, 2003.
DOI : 10.1109/IVS.2003.1212963

Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, International Conference on Machine Learning, pp.148-156, 1996.

D. Gavrila, Pedestrian Detection from a Moving Vehicle, Proceedings of European Conference on Computer Vision, pp.37-49, 2000.
DOI : 10.1007/3-540-45053-X_3

C. Gentile, O. I. Camps, and M. Sznaier, Segmentation for robust tracking in the presence of severe occlusion, pp.483-488, 2001.

M. Genton, Classes of kernels for machine learning : A statistics perspective, Journal of Machine Learning Research, vol.2, pp.299-312, 2001.

S. Gold and A. Rangarajan, A graduated assignment algorithm for graph matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.4, pp.377-388, 1996.
DOI : 10.1109/34.491619

K. Grauman and T. Darrell, The pyramid match kernel: discriminative classification with sets of image features, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.239

T. Gärtner, P. Flach, and S. Wrobel, On Graph Kernels: Hardness Results and Efficient Alternatives, Lecture Notes in Artificial Intelligence, vol.2777, issue.1, pp.129-143, 2003.
DOI : 10.1007/978-3-540-45167-9_11

V. Guigue, A. Rakotomamonjy, and S. Canu, Classification de signaux invariante en translation, 20e colloque GRETSI sur le traitement du signal et des images, 2005.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

P. Hayton, B. Schölkopf, L. Tarassenko, and P. Anuzis, Support vector novelty detection applied to jet engine vibration spectra, Neural Information Processing Systems, pp.946-952, 2000.

D. D. Hoffman and B. E. Flinchbaugh, The interpretation of biological motion, Biological Cybernetics, pp.195-204, 1982.

K. P. Berthold, B. G. Horn, and . Schunck, Determining optical flow, Artificial Intelligence, vol.17, pp.185-203, 1981.

C. Hsu and C. Lin, A comparison of methods for multi-class support vector machines, 2001.

P. Daniel, G. A. Huttenlocher, W. Klanderman, and . Rucklidge, Comparing images using the hausdorff distance, IEEE Trans. Pattern Anal. Mach. Intell, vol.15, issue.9, pp.850-863, 1993.

S. Ioffe and D. Forsyth, Probabilistic methods for finding people, International Journal of Computer Vision, vol.43, issue.1, pp.45-68, 2001.
DOI : 10.1023/A:1011179004708

H. Kashima and Y. Tsuboi, Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015383

H. Kashima, K. Tsuda, and A. Inokuchi, Marginalized kernels between labeled graphs, Proceedings of the Twentieh International Conference on Machine Learning, 2003.

Y. Keselman, A. Shokoufandeh, F. Demirci, and S. Dickinson, Many-to-many feature matching using spherical coding of directed graphs, Proceedings, 8th European Conference on Computer Vision, pp.322-335, 2004.

G. Kimeldorf and G. Wahba, Some results on Tchebycheffian spline functions, Journal of Mathematical Analysis and Applications, vol.33, issue.1, pp.82-95, 1971.
DOI : 10.1016/0022-247X(71)90184-3

R. Kondor and T. Jebara, A kernel between sets of vectors, Proc. of ICML-2003, 2003.

K. Konolige, Small Vision Systems: Hardware and Implementation, Eighth International Symposium on Robotics Research, 1997.
DOI : 10.1007/978-1-4471-1580-9_19

R. Labayrade, D. Aubert, and J. Tarel, Real time obstacle detection in stereovision on non flat road geometry through "v-disparity" representation, Intelligent Vehicle Symposium, 2002. IEEE, pp.646-651, 2002.
DOI : 10.1109/IVS.2002.1188024

C. Lantuejoul and S. Beucher, On the use of the geodesic metric in image analysis, Journal of Microscopy, vol.121, issue.1, pp.39-49, 1981.
DOI : 10.1111/j.1365-2818.1981.tb01197.x

E. Bastian-leibe, B. Seemann, and . Schiele, Pedestrian detection in crowded scenes, CVPR '05 : Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05, pp.878-885, 2005.

V. Lemonde and M. Devy, Détection d'obstacles par stéréovision sur véhicules intelligents, Congrès des jeunes chercheurs en vision par ordinateur, ORASIS' 05, 2005.

H. Lodhi, J. Shawe-taylor, N. Cristianini, and C. Watkins, Text classification using string kernels, Neural Information Processing Systems, pp.563-569, 2000.

G. Loosli, S. Canu, and L. Bottou, Training invariant support vector machines using selective sampling, 2005.

G. Loosli, S. Canu, S. V. Vishwanathan, A. J. Smola, and M. Chattopadhyay, Une boîte à outils rapide et simple pour les svm, Conférence d'Apprentissage, pp.113-128, 2004.
DOI : 10.3166/ria.19.741-767

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

P. Mahé, N. Ueda, T. Akutsu, J. Perret, and J. Vert, Extensions of marginalized graph kernels, Twenty-first international conference on Machine learning , ICML '04, pp.552-559, 2004.
DOI : 10.1145/1015330.1015446

M. Mählisch, M. Oberländer, O. Löhlein, D. Gavrila, and W. Ritter, A multiple detector approach to low-resolution FIR pedestrian recognition, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.325-330, 2005.
DOI : 10.1109/IVS.2005.1505123

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, pp.43-72, 2005.
DOI : 10.1007/s11263-005-3848-x

URL : https://hal.archives-ouvertes.fr/inria-00548528

E. Nowak, F. Jurie, and B. Triggs, Sampling Strategies for Bag-of-Features Image Classification, European Conference on Computer Vision, 2006.
DOI : 10.1007/11744085_38

URL : https://hal.archives-ouvertes.fr/hal-00203752

. Cheng-soon, X. Ong, S. Mary, A. Canu, and . Smola, Learning with non-positive kernels, Proceedings of the 21st International Conference on Machine Learning, pp.639-646, 2004.

. Cheng-soon, A. J. Ong, R. C. Smola, and . Williamson, Learning the kernel with hyperkernels, Journal of Machine Learning Research, vol.6, pp.1043-1071, 2005.

M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, Pedestrian detection using wavelet templates, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.193, 1997.
DOI : 10.1109/CVPR.1997.609319

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.182

C. Papageorgiou and T. Poggio, Trainable pedestrian detection, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), pp.35-39, 1999.
DOI : 10.1109/ICIP.1999.819462

S. Philipp-foliguet and M. Lekkat, Recherche d'images à partir d'une requête partielle utilisant la disposition des régions, Actes du colloque RFIA'04, 2004.

T. Poggio and F. Girosi, Networks for approximation and learning, Proceedings of the IEEE, pp.1481-1497, 1990.
DOI : 10.1109/5.58326

A. Rakotomamonjy and F. Suard, Sélection de variables par svm : application à la détection de piétons, RFIA04, 2004.

C. Di and R. , Recognition of shapes by attributed skeletal graphs, Pattern Recognition, vol.37, issue.1, pp.21-31, 2004.

C. Di, R. , and G. Rodriguez, Recognition of shapes by morphological attributed relational graphs, 2002.

N. Boujemaa, S. Boughorbel, and C. Vertan, Soft color signatures for image retrieval by content, pp.2001-394, 2001.

B. Le, S. , and H. Bunke, Combining svm and graph matching in a multiple classifier system for image content recognition, Workshop on Statistical Pattern Recognition (S+SSPR'06) of the IAPR International Conference on Pattern Recognition (ICPR'06), 2006.

B. Schiele and J. L. Crowley, Object recognition using multidimensional receptive field histograms, ECCV (1), pp.610-619, 1996.
DOI : 10.1007/BFb0015571

URL : https://hal.archives-ouvertes.fr/tel-00004962

J. Serra, Morphologie mathématique. Traité d'Informatique Géologique, pp.194-238, 1972.

A. Shashua, Y. Gdalyahu, and G. Hayon, Pedestrian detection for driving assistance systems: single-frame classification and system level performance, IEEE Intelligent Vehicles Symposium, 2004, 2004.
DOI : 10.1109/IVS.2004.1336346

K. Siddiqi, A. Shokoufandeh, S. J. Dickinson, and S. W. Zucker, Shock graphs and shape matching, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp.13-32, 1999.
DOI : 10.1109/ICCV.1998.710722

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.3873

M. Stricker and M. Swain, The capacity of color histogram indexing, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, pp.704-708, 1994.
DOI : 10.1109/CVPR.1994.323774

F. Suard, V. Guigue, A. Rakotomamonjy, and A. Bensrhair, Pedestrian detection using stereo-vision and graph kernels, IEEE Proceedings. Intelligent Vehicles Symposium, 2005., pp.267-272, 2005.
DOI : 10.1109/IVS.2005.1505113

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.665.4990

F. Suard, A. Rakotomamonjy, and A. Bensrhair, Détection de piétons par stéréovision et noyaux de graphes, pp.686-686, 2005.

F. Suard, A. Rakotomamonjy, and A. Bensrhair, Object Categorization Using Kernels Combining Graphs and Histograms of Gradients, International Conference on Image Analysis and Recognition, pp.23-34, 2006.
DOI : 10.1007/11867661_3

F. Suard, A. Rakotomamonjy, A. Bensrhair, and A. Broggi, Pedestrian Detection using Infrared images and Histograms of Oriented Gradients, 2006 IEEE Intelligent Vehicles Symposium, pp.206-212, 2006.
DOI : 10.1109/IVS.2006.1689629

M. Szarvas, U. Sakai, and J. Ogata, Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks, 2006 IEEE Intelligent Vehicles Symposium, pp.213-218, 2006.
DOI : 10.1109/IVS.2006.1689630

A. Torsello, Matching Hierarchical Structures for Shape Recognition, 2004.

G. Toulminet, Extraction des contours 3D des obstacles par stéréovision pour l'aide à la conduite automobile, 2002.

K. Tsuda, T. Kin, and K. Asai, Marginalized kernels for biological sequences, Bioinformatics, vol.18, issue.Suppl 1, pp.268-275, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S268

R. Unnthorsson, T. Philip-runarsson, and M. T. Jonsson, Model selection in one class nu-svms using rbf kernels, 16th conference on Condition Monitoring and Diagnostic Engineering Management, 2003.

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.511-518, 2001.
DOI : 10.1109/CVPR.2001.990517

P. Viola, M. Jones, and D. Snow, Pedetrian using patterns of motions and appearance, IEEE Int. Conf on Computer Vision, pp.734-741, 2003.

S. V. Vishwanathan and A. J. Smola, Fast kernels on strings and trees, Neural Information Processing Systems Conference, 2002.

C. Wallraven, B. Caputo, and A. Graf, Recognition with local features: the kernel recipe, Proceedings Ninth IEEE International Conference on Computer Vision, pp.257-265, 2003.
DOI : 10.1109/ICCV.2003.1238351

L. Wiskott, J. Fellous, N. Krüger, C. Von, and . Malsburg, Face recognition by elastic bunch graph matching, Proc. 7th Intern. Conf. on Computer Analysis of Images and Patterns, CAIP'97, pp.456-463, 1997.
DOI : 10.1007/3-540-63460-6_150

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.120.4805

L. Wolf and A. Shashua, Learning over sets using kernel principal angles, Journal of Machine Learning Research, vol.4, pp.913-931, 2003.

F. Xu, X. Liu, and K. Fujimura, Pedestrian Detection and Tracking With Night Vision, IEEE Transactions on Intelligent Transportation Systems, vol.6, issue.1, pp.6-7, 2005.
DOI : 10.1109/TITS.2004.838222

L. Zhao, Dressed Human Modeling, Detection, Detection, and Parts Localization, 2001.

L. Zhao and C. Thorpe, Stereo- and neural network-based pedestrian detection, IEEE Transactions on Intelligent Transportation Systems, vol.1, issue.3, pp.148-154, 2000.
DOI : 10.1109/6979.892151