Compressive image super-resolution, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, pp.1235-1242, 2009. ,
DOI : 10.1109/ACSSC.2009.5469968
Single-Image Super-Resolution Using Compressive Sensing, International Journal of Image Processing and Visual Communication, vol.1, issue.4, pp.8-15, 2013. ,
Understanding Compressive Sensing and Sparse Representation-Based Super-Resolution, IEEE Transactions on Circuits and Systems for Video Technology, pp.778-789, 2012. ,
DOI : 10.1109/TCSVT.2011.2180773
Image Super-Resolution Via Sparse Representation, IEEE Transactions on Image Processing, vol.19, issue.11, pp.2861-2873, 2010. ,
DOI : 10.1109/TIP.2010.2050625
Single-Image Super-Resolution via Linear Mapping of Interpolated Self-Examples, IEEE Transactions on Image Processing, vol.23, issue.12, pp.5334-5347, 2014. ,
DOI : 10.1109/TIP.2014.2364116
URL : https://hal.archives-ouvertes.fr/hal-01088753
Super-resolution through neighbor embedding, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.275-282, 2004. ,
DOI : 10.1109/CVPR.2004.1315043
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization, IEEE Transactions on Image Processing, vol.20, issue.7, pp.1838-1857, 2011. ,
DOI : 10.1109/TIP.2011.2108306
Nonlocally Centralized Sparse Representation for Image Restoration, IEEE Transactions on Image Processing, vol.22, issue.4, pp.1620-1630, 2013. ,
DOI : 10.1109/TIP.2012.2235847
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.426
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, IEEE Transactions on Image Processing, vol.23, issue.6, pp.2569-2582, 2014. ,
DOI : 10.1109/TIP.2014.2305844
Single image super-resolution using sparse representations with structure constraints, 2014 IEEE International Conference on Image Processing (ICIP), pp.3862-3866, 2014. ,
DOI : 10.1109/ICIP.2014.7025784
URL : https://hal.archives-ouvertes.fr/hal-00995052
Geometry-Aware Neighborhood Search for Learning Local Models for Image Superresolution, IEEE Transactions on Image Processing, vol.25, issue.3, pp.1354-1367, 2016. ,
DOI : 10.1109/TIP.2016.2522303
URL : https://hal.archives-ouvertes.fr/hal-01388955
Limits on super-resolution and how to break them, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.9, pp.1167-1183, 2002. ,
DOI : 10.1109/TPAMI.2002.1033210
Sur les problèmes aux dérivés partielles et leur signification physique, Princeton University Bulletin, vol.13, pp.49-52, 1902. ,
Solutions of ill-posed problems, 1977. ,
Multirate systems and filter banks, 1993. ,
Compressive sensing, ser, Publicações Matemáticas, 27 Colóquio Brasileiro de Matemática, 2009. ,
Image Super-Resolution as Sparse Representation of Raw Image Patches, Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, 2008. ,
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3781, 2006. ,
DOI : 10.1109/TIP.2006.881969
<tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006. ,
DOI : 10.1109/TSP.2006.881199
Sparse Representation for Color Image Restoration, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.53-69, 2008. ,
DOI : 10.1109/TIP.2007.911828
Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA), Applied and Computational Harmonic Analysis, vol.19, issue.3, pp.340-358, 2005. ,
DOI : 10.1016/j.acha.2005.03.005
An overview of JPEG-2000, Proceedings DCC 2000. Data Compression Conference, pp.523-541 ,
DOI : 10.1109/DCC.2000.838192
Sparse representation for signal classification, 2006. ,
An Introduction To Compressive Sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.21-30, 2008. ,
DOI : 10.1109/MSP.2007.914731
Learning contextaware sparse representation for single image super-resolution, 18th IEEE International Conference on Image Processing, pp.1349-1352, 2011. ,
Resolution enhancement based on learning the sparse association of image patches, Pattern Recognition Letters, vol.31, issue.1, pp.1-10, 2010. ,
DOI : 10.1016/j.patrec.2009.09.004
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006. ,
DOI : 10.1109/TIT.2005.862083
Single-Pixel Imaging via Compressive Sampling, IEEE Signal Processing Magazine, vol.25, issue.2, pp.83-91, 2008. ,
DOI : 10.1109/MSP.2007.914730
Fast compressive imaging using scrambled block Hadamard ensemble, 16th European Signal Processing Conference (EUSIPCO), 2008. ,
Compressive imaging of color images, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ,
DOI : 10.1109/ICASSP.2009.4959820
Edge Guided Reconstruction for Compressive Imaging, SIAM Journal on Imaging Sciences, vol.5, issue.3, pp.809-834, 2012. ,
DOI : 10.1137/110837309
Compressive Acquisition of Dynamic Scenes, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.6311, issue.1, pp.129-142, 2010. ,
DOI : 10.1007/978-3-642-15549-9_10
Compressive video sampling, 16th European Signal Processing Conference (EUSIPCO), 2009. ,
A multiscale framework for Compressive Sensing of video, 2009 Picture Coding Symposium, 2009. ,
DOI : 10.1109/PCS.2009.5167440
Multichannel image estimation via simultaneous orthogonal matching pursuit, IEEE Workshop on Statistical Signal Processing (SSP), 2007. ,
Compressed Sensing Image Reconstruction Via Recursive Spatially Adaptive Filtering, 2007 IEEE International Conference on Image Processing, 2007. ,
DOI : 10.1109/ICIP.2007.4379013
A single-pixel terahertz imaging system based on compressive sensing, Applied Physics Letters, vol.93, issue.12, pp.101-105, 2008. ,
Terahertz imaging with compressed sensing and phase retrieval, Optics Letters, vol.33, issue.9, pp.974-976, 2008. ,
DOI : 10.1364/OL.33.000974
A 2D camera design with a single-pixel detector, 2009 34th International Conference on Infrared, Millimeter, and Terahertz Waves, 2009. ,
DOI : 10.1109/ICIMW.2009.5324725
Compressive Sensing for Background Subtraction, Computer Vision (ECCV), European Conference on, pp.155-168, 2008. ,
DOI : 10.1007/978-3-540-88688-4_12
A manifold lifting algorithm for multi-view compressive imaging, 2009 Picture Coding Symposium, pp.1-4, 2009. ,
DOI : 10.1109/PCS.2009.5167356
Single-pixel remote sensing, IEEE Geoscience and Remote Sensing Letters, vol.6, issue.2, pp.199-203, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00564943
Image fusion by compressive sensing, 17th International Conference on Geoinformatics, 2009. ,
Improved Iterative Curvelet Thresholding for Compressed Sensing .pdf, IEEE Transactions on Instrumentation and Measurement, vol.59, issue.10, pp.1-11, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00678434
Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics, vol.1, issue.4, pp.433-459, 2010. ,
DOI : 10.1002/wics.101
URL : https://hal.archives-ouvertes.fr/hal-01259094
Sparse Principal Component Analysis, Journal of Computational and Graphical Statistics, vol.15, issue.2, pp.265-286, 2006. ,
DOI : 10.1198/106186006X113430
Principal Geodesic Analysis for the Study of Nonlinear Statistics of Shape, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.995-1005, 2004. ,
DOI : 10.1109/TMI.2004.831793
Riemannian geometry for the statistical analysis of diffusion tensor data, Signal Processing, vol.87, issue.2, pp.250-262, 2007. ,
DOI : 10.1016/j.sigpro.2005.12.018
Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency, Journal of Visual Communication and Image Representation, vol.4, issue.4, pp.324-335, 1993. ,
DOI : 10.1006/jvci.1993.1030
SoftCuts: A Soft Edge Smoothness Prior for Color Image Super- Resolution, IEEE Transactions on Image Processing, vol.18, issue.5, pp.969-981, 2009. ,
Example-based super-resolution, IEEE Computer Graphics and Applications, vol.22, issue.2, pp.56-65, 2002. ,
DOI : 10.1109/38.988747
New edge-directed interpolation, IEEE Transactions on Image Processing, vol.10, issue.10, pp.1521-1527, 2001. ,
Exploiting the sparse derivative prior for super-resolution and image demosaicing, IEEE Workshop on Statistical and Computational Theories of Vision, pp.1-24, 2003. ,
Soft Edge Smoothness Prior for Alpha Channel Super Resolution, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007. ,
DOI : 10.1109/CVPR.2007.383028
Image up-sampling using total-variation regularization with a new observation model, IEEE Transactions on Image Processing, vol.14, issue.10, pp.1647-1659, 2005. ,
DOI : 10.1109/TIP.2005.851684
Fast image/video upsampling, ACM Transactions on Graphics, vol.27, issue.5 1, 2008. ,
Single image super-resolution using Gaussian process regression, CVPR 2011, pp.449-456, 2011. ,
DOI : 10.1109/CVPR.2011.5995713
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression, IEEE Transactions on Image Processing, vol.21, issue.11, pp.4544-4556, 2012. ,
DOI : 10.1109/TIP.2012.2208977
Image Hallucination Using Neighbor Embedding over Visual Primitive Manifolds, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-7, 2007. ,
DOI : 10.1109/CVPR.2007.383001
Neighbor embedding based super-resolution algorithm through edge detection and feature selection, Pattern Recognition Letters, vol.30, issue.5, pp.494-502, 2009. ,
DOI : 10.1016/j.patrec.2008.11.008
On Single Image Scale-Up Using Sparse-Representations, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS, vol.6920, issue.1, pp.711-730, 2012. ,
DOI : 10.1007/978-3-642-27413-8_47
Image Super- Resolution With Sparse Neighbor Embedding, IEEE Transactions on Image Processing, vol.21, issue.7, pp.3194-3205, 2012. ,
Centralized sparse representation for image restoration, 2011 International Conference on Computer Vision, pp.1259-1266, 2011. ,
DOI : 10.1109/ICCV.2011.6126377
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.1382-94, 2013. ,
DOI : 10.1109/TIP.2012.2231086
Weighted overcomplete denoising, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, pp.1992-1996, 1992. ,
DOI : 10.1109/ACSSC.2003.1292330
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images, IEEE Transactions on Image Processing, vol.6, issue.12, pp.1621-1633, 1997. ,
DOI : 10.1109/83.650116
Sparse Super-Resolution with Space Matching Pursuits, SPARS'09 -Signal Processing with Adaptive Sparse Structured Representations, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00369620
Image hallucination with primal sketch priors, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.729-765, 2003. ,
Signal recovery from random projections, Proceedings of SPIE, pp.76-86, 2005. ,
Efficient sparse coding algorithms Advances in neural information, p.801, 2007. ,
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004. ,
DOI : 10.1002/cpa.20042
Image Super-Resolution by TV-Regularization and Bregman Iteration, Journal of Scientific Computing, vol.7, issue.6, pp.367-382, 2008. ,
DOI : 10.1007/s10915-008-9214-8
A note on the gradient of a multi-image, Computer Vision, Graphics, and Image Processing, vol.33, issue.1, pp.116-125, 1986. ,
DOI : 10.1016/0734-189X(86)90223-9
Principles of filter design Handbook of computer vision and applications, pp.125-151, 1999. ,
Non-local adaptive structure tensors, Image and Vision Computing, vol.29, issue.11, pp.730-743, 2011. ,
DOI : 10.1016/j.imavis.2011.07.007
Theory of Edge Detection, Proceedings of the Royal Society B: Biological Sciences, vol.207, issue.1167, pp.187-217, 1980. ,
DOI : 10.1098/rspb.1980.0020
Spatio-temporal image processing: theory and scientific applications, 1993. ,
DOI : 10.1007/3-540-57418-2
Anisotropic diffusion in image processing, ser. ECMI Series, 1998. ,
Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004. ,
DOI : 10.1109/TIP.2003.819861
Sparsity-based image denoising via dictionary learning and structural clustering, CVPR 2011, pp.457-464, 2011. ,
DOI : 10.1109/CVPR.2011.5995478
Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006. ,
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?, IEEE Transactions on Information Theory, vol.52, issue.12, pp.5406-5425, 2006. ,
DOI : 10.1109/TIT.2006.885507
Example-Driven Manifold Priors for Image Deconvolution, IEEE Transactions on Image Processing, vol.20, issue.11, pp.3086-3096, 2011. ,
DOI : 10.1109/TIP.2011.2145386
Poisson Noise Reduction with Non-local PCA, Journal of Mathematical Imaging and Vision, vol.15, issue.2, pp.279-294, 2014. ,
DOI : 10.1007/s10851-013-0435-6
URL : https://hal.archives-ouvertes.fr/hal-00957837
Image denoising with block-matching and 3D filtering, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, pp.606-414, 2006. ,
DOI : 10.1117/12.643267
Denoising of multispectral images via nonlocal groupwise spectrum-PCA, Conference on Colour in Graphics, Imaging, and Vision, pp.261-266, 2010. ,
The Nonlinear Statistics of High-Contrast Patches in Natural Images, International Journal of Computer Vision, vol.54, issue.1/2, pp.83-103, 2003. ,
DOI : 10.1023/A:1023705401078
Manifold models for signals and images, Computer Vision and Image Understanding, vol.113, issue.2, pp.249-260, 2008. ,
DOI : 10.1016/j.cviu.2008.09.003
Overcoming noise, avoiding curvature: Optimal scale selection for tangent plane recovery, 2012 IEEE Statistical Signal Processing Workshop (SSP), pp.892-895, 2012. ,
DOI : 10.1109/SSP.2012.6319851
Tangent space estimation for smooth embeddings of Riemannian manifolds, Information and Inference, vol.2, issue.1, pp.69-114, 2013. ,
DOI : 10.1093/imaiai/iat003
Replicator Graph Clustering, Procedings of the British Machine Vision Conference 2013, pp.38-39 ,
DOI : 10.5244/C.27.38
Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.888-905, 2000. ,
On Spectral Clustering: Analysis and an algorithm, Proceedings Advances in Neural Information Processing Systems, pp.849-856, 2001. ,
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation, vol.15, issue.6, pp.1373-1396, 2003. ,
DOI : 10.1126/science.290.5500.2319
Geodesic K-means clustering, 2008 19th International Conference on Pattern Recognition, pp.1-4, 2008. ,
DOI : 10.1109/ICPR.2008.4761241
A novel graph-based k-means for nonlinear manifold clustering and representative selection, Neurocomputing, vol.143, pp.1-14, 2014. ,
DOI : 10.1016/j.neucom.2014.05.067
Clustering through ranking on manifolds, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.73-80, 2005. ,
DOI : 10.1145/1102351.1102361
Learning with Local and Global Consistency, Proceedings Advances in Neural Information Processing Systems 16 (NIPS), pp.321-328, 2004. ,
Nearest-neighbor search algorithms on non-Euclidean manifolds for computer vision applications, Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP '10, pp.282-289, 2010. ,
DOI : 10.1145/1924559.1924597
Fast Approximate Nearest Neighbor Methods for Non-Euclidean Manifolds with Applications to Human Activity Analysis in Videos, Proceedings European Conference on Computer Vision (ECCV), pp.735-748, 2010. ,
DOI : 10.1007/978-3-642-15552-9_53
Manifold clustering, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.648-653, 2005. ,
DOI : 10.1109/ICCV.2005.149
Sparse Manifold Clustering and Embedding, Proceedings Advances in Neural Information Processing Systems 24 (Nips), pp.55-63, 2011. ,
Clustering and dimensionality reduction on Riemannian manifolds, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-7, 2008. ,
DOI : 10.1109/CVPR.2008.4587422
FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, vol.10, issue.2-3, pp.191-203, 1984. ,
DOI : 10.1016/0098-3004(84)90020-7
Soft Geodesic Kernel K-Means, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.429-432, 2007. ,
DOI : 10.1109/ICASSP.2007.366264
Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity, IEEE Transactions on Image Processing, vol.21, issue.5, pp.2481-2499, 2012. ,
A note on two problems in connexion with graphs, Numerische Mathematik, vol.4, issue.1, pp.269-271, 1959. ,
DOI : 10.1007/BF01386390
Curvature analysis of pattern transformation manifolds, 2010 IEEE International Conference on Image Processing, pp.2689-2692 ,
DOI : 10.1109/ICIP.2010.5651945
FSIM: A Feature Similarity Index for Image Quality Assessment, IEEE Transactions on Image Processing, vol.20, issue.8, pp.2378-2386, 2011. ,
DOI : 10.1109/TIP.2011.2109730
Image restoration through l0 analysis-based sparse optimization in tight frames, 2009 16th IEEE International Conference on Image Processing (ICIP), pp.3909-3912, 2009. ,
DOI : 10.1109/ICIP.2009.5413975
BM3D Frames and Variational Image Deblurring, IEEE Transactions on Image Processing, vol.21, issue.4, pp.1715-1728, 2012. ,
DOI : 10.1109/TIP.2011.2176954
From Local Kernel to Nonlocal Multiple-Model Image Denoising, International Journal of Computer Vision, vol.11, issue.1, pp.1-32, 2010. ,
DOI : 10.1007/s11263-009-0272-7
Non-local sparse models for image restoration, 2009 IEEE 12th International Conference on Computer Vision, pp.2272-2279, 2009. ,
DOI : 10.1109/ICCV.2009.5459452
From learning models of natural image patches to whole image restoration, 2011 International Conference on Computer Vision, pp.479-486 ,
DOI : 10.1109/ICCV.2011.6126278
Direct Sparse Deblurring, Proceedings IEEE International Conference on Image Processing (ICIP), pp.1-12, 2008. ,
DOI : 10.1007/s10851-010-0220-8
Learning Unions of Orthonormal Bases with Thresholded Singular Value Decomposition, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.293-296, 2005. ,
DOI : 10.1109/ICASSP.2005.1416298
URL : https://hal.archives-ouvertes.fr/inria-00564483
Approximation and Compression With Sparse Orthonormal Transforms, IEEE Transactions on Image Processing, vol.24, issue.8, pp.2328-2343, 2015. ,
DOI : 10.1109/TIP.2015.2414879
Block coordinate relaxation methods for nonparametric signal denoising with wavelet dictionaries, Journal of Computational and Graphical Statistics, vol.9, issue.2, pp.361-379, 2000. ,
Tangent-based manifold approximation with locally linear models, Signal Processing, vol.104, pp.232-247, 2014. ,
DOI : 10.1016/j.sigpro.2014.03.047
PREVIS??O DE VAZ??O DA BACIA DO RIBEIR??O JO??O LEITE UTILIZANDO REDES NEURAIS COM TREINAMENTO LEVENBERG-MARQUARDT, Anais do 9. Congresso Brasileiro de Redes Neurais, 2009. ,
DOI : 10.21528/CBRN2009-175
Cost Optimization of a Localized Irrigation System Using Genetic Algorithms, Intelligent Data Engineering and Automated Learning -IDEAL 2010, pp.29-36, 2010. ,
DOI : 10.1007/978-3-642-15381-5_4
PREVIS??O DE VAZ??O DA BACIA DO RIBEIRO JO??O LEITE UTILIZANDO REDES NEURAIS ARTIFICIAIS, IRRIGA, vol.16, issue.3, pp.339-350, 2011. ,
DOI : 10.15809/irriga.2011v16n3p339
Influ??ncia do declive no custo total de uma rede de irriga????o localizada, Revista Brasileira de Agricultura Irrigada, vol.6, issue.3, pp.247-258, 2012. ,
DOI : 10.7127/rbai.v6n300368
and the affinity between d l and d i is a * il . The intermediate node d l contributes by the product a l a * il to the overall affinity between y j and d i . The sample d l is just another intermediate node like d l . Summing the affinities via all possible intermediate nodes (i.e., all training samples), p.80 ,
98 4.11 Test images for denoising, Test images for deblurring: Couple, Fingerprint (F. Print), p.100 ,
Test images for super-resolution: Butterfly, Bike, Hat, Plants, p.123 ,
) results for the luminance components of superresolved HR images for different super-resolution scenarios The scenarios are grouped according to the clustering method (K-means and GOC methods), PSNR, p.117 ,
A Geometry-aware Dictionary Learning Strategy based on Sparse Representations ,
Geometry-Aware Neighborhood Search for Learning Local Models for Image Superresolution, IEEE Transactions on Image Processing, vol.25, issue.3, pp.1354-1367, 2016. ,
DOI : 10.1109/TIP.2016.2522303
URL : https://hal.archives-ouvertes.fr/hal-01388955
Quantization Noise on Image Reconstruction Using Model-Based Compressive Sensing, IEEE Latin America Transactions, vol.13, issue.4, pp.1167-1177, 2015. ,
DOI : 10.1109/TLA.2015.7106372
Single image super-resolution using sparse representations with structure constraints, 2014 IEEE International Conference on Image Processing (ICIP) ,
DOI : 10.1109/ICIP.2014.7025784
URL : https://hal.archives-ouvertes.fr/hal-00995052