| Detection tests using sparse models, with application to hyperspectral data S Paris, D Mary, A Ferrari IEEE transactions on signal processing 61 (6), 1481-1494, 2013 | 32 | 2013 |
| Fine-grained object recognition in underwater visual data C Spampinato, S Palazzo, PH Joalland, S Paris, H Glotin, K Blanc, ... Multimedia Tools and Applications 75, 1701-1720, 2016 | 30 | 2016 |
| Constrained likelihood ratios for detecting sparse signals in highly noisy 3D data S Paris, RFR Suleiman, D Mary, A Ferrari 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 15 | 2013 |
| Contribution of a classifier of skin lesions to the dermatologist's decision Y Wazaefi, S Paris, B Fertil 2012 3rd International Conference on Image Processing Theory, Tools and …, 2012 | 15 | 2012 |
| Sparse coding for histograms of local binary patterns applied for image categorization: Toward a bag-of-scenes analysis S Paris, X Halkias, H Glotin Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 13 | 2012 |
| Sparsity-based composite detection tests. Application to astrophysical hyperspectral data S Paris, D Mary, A Ferrari, S Bourguignon 2011 19th European Signal Processing Conference, 1909-1913, 2011 | 11 | 2011 |
| Composite hypothesis tests for sparse parameters S Paris, D Mary, A Ferrari 2011 IEEE Statistical Signal Processing Workshop (SSP), 737-740, 2011 | 8 | 2011 |
| PDR and LRMAP detection tests applied to massive hyperspectral data S Paris, D Mary, A Ferrari 2011 4th IEEE International Workshop on Computational Advances in Multi …, 2011 | 7 | 2011 |
| Learning from examples to automatically cluster pigmented skin lesions Y Wazaefi, S Paris, J Lefevre, C Gaudy, JJ Grob, B Fertil 2013 IEEE 10th International Symposium on Biomedical Imaging, 153-156, 2013 | 4 | 2013 |
| On the probability distribution of a moving target. Asymptotic and non-asymptotic results M Chouchane, S Paris, F Le Gland, C Musso, DT Pham 14th International Conference on Information Fusion, 1-8, 2011 | 4 | 2011 |
| Sparsity-based detection strategies for faint signals in noise: application to astrophysical hyperspectral data S Paris Université Nice Sophia Antipolis; Università degli studi La Sapienza (Rome), 2013 | 3 | 2013 |
| Detection of astrophysical sources in hyperspectral data. applications to the muse instrument D Mary, A Ferrari, S Paris 2014 IEEE International Conference on Image Processing (ICIP), 6031-6035, 2014 | 2 | 2014 |
| A proposal of a solution for age band prediction from human faces Y Lufimpu-Luviya, D Merad, S Paris, B Fertil 2013 10th IEEE International Conference on Advanced Video and Signal Based …, 2013 | 2 | 2013 |
| MAP-based sparse detection strategies. Application to the hyperspectral data of the MUSE instrument. S Paris, D Mary, A Ferrari ADA7-Seventh Conference on Astronomical Data Analysis, held in Cargése …, 2012 | 1 | 2012 |
| Consensus clustering from experts' partitions for patients' nevi: Model the Ugly Duckling Y Wazaefi, Y Bruneu, J Lefèvre, G Menegaz, G Paggetti, A Le Troter, ... Proceedings of the International Conference on Data Science (ICDATA), 1, 2012 | 1 | 2012 |
| Sur des tests d’hypothèses composites pour des paramètres parcimonieux S Paris, D Mary, A Ferrari XXIII Colloque GRETSI, 5, 2011 | 1 | 2011 |
| Introduction to detection Application to MUSE D Mary, S Paris | | 2017 |
| Méthodes de détection parcimonieuses pour signaux faibles dans du bruit: application à des données hyperspectrales de type astrophysique S Paris Nice, 2013 | | 2013 |
David MaryUniversité de Nice在 unice.fr 的电子邮件经过验证
