Articles
2024
Moufidi, A., Rousseau, D. and Rasti, P. (2024). Toward comprehensive short utterances manipulations detection in videos. Multimedia Tools and Applications, 1-14. doi: 10.1007/s11042-024-20284-x
Céré, C., Curcio, V., Dorez, H., Debreuque, M., Franconi, F. and Rousseau, D. (2024). Quantitative MRI for brain lesion diagnosis in dogs and cats: A comprehensive overview. Veterinary Radiology & Ultrasound. doi: org/10.1111/vru.13434
Kouakou, H., Goulart, J.H.D.M., Vitale, R., Oberlin, T., Rousseau, D., Ruckebusch, C. and Dobigeon, N. (2024). On-the-fly spectral unmixing based on Kalman filtering. Chemometrics and Intelligent Laboratory Systems. doi: 10.1016/j.chemolab.2024.105252
Bouillon, P., Fanciullino, A.L., Belin, E., Hanteville, S., Muranty, H., Bernard, F. and Celton, J.M. (2024). Tracing the color: quantitative trait loci analysis reveals new insights into red-flesh pigmentation in apple (Malus domestica). Horticulture research, 11(8). doi: 10.1093/hr/uhae171
Cordier, M., Rasti, P., Torres, C., and Rousseau, D. (2024). Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss. Plant Phenomics, 6. doi: 10.34133/plantphenomics.0204
Macia, F.M., Possamai, T., Dorne, MA., Rousseau D. et al. Phenotyping grapevine resistance to downy mildew: deep learning as a promising tool to assess sporulation and necrosis. Plant Methods 20, 90 (2024). doi: 10.1186/s13007-024-01220-4
Bouillon, P., Fanciullino, A.L., Belin, E., Breard, D., Boisard, S., Bonnet, B., Hanteville, S., Bernard, F. and Celton, J.M. (2024). Image analysis and polyphenol profiling unveil red‑flesh apple phenotype complexity. Plant Methods (2024) 20:71. https://doi.org/10.1186/s13007-024-01196-1
Hamdy, S., Charrier, A., Corre, L. L., Rasti, P., and Rousseau, D. (2024). Toward robust and high-throughput detection of seed defects in X-ray images via deep learning. Plant Methods, 20(1), 63. http://dx.doi.org/10.1186/s13007-024-01195-2
Gilet, V., Mabilleau, G., Loumaigne, M., Coic, L., Vitale, R., Oberlin, T., ... and Rousseau, D. (2024). Superpixels meet essential spectra for fast Raman hyperspectral microimaging. Optics Express, 32(1), 932-948. http://dx.doi.org/10.1364/OE.509736
2023
Coic, L., Vitale, R., Moreau, M., Rousseau, D., de Morais Goulart, J. H., Dobigeon, N. and Ruckebusch, C. (2023). Assessment of essential information in the Fourier domain to accelerate raman hyperspectral microimaging. Analytical Chemistry, 95(42), 15497-15504. doi: 10.26434/chemrxiv-2023-0m36t
Pernet, A., Eid, R., Landès, C., Benoît, E., Santagostini, P., Marie-Magdelaine, J., Clotault, J., El Ghaziri, A. and Bourbeillon, J. (2023). Construction of a semantic distance for inferring structure of the variability between 19th century Rosa cultivars. Acta Hortic. 1384, 477-484. https://doi.org/10.17660/ActaHortic.2023.1384.60
Moufidi, A., Rousseau, D., & Rasti, P. (2023). Attention-Based Fusion of Ultrashort Voice Utterances and Depth Videos for Multimodal Person Identification. Sensors, 23(13), 5890. https://doi.org/10.3390/s23135890
Cordier, M., Torres, C., Rasti, P., & Rousseau, D. (2023). On the Use of Circadian Cycles to Monitor Individual Young Plants. Remote Sensing, 15(11), 2704. https://doi.org/10.3390/rs15112704
Jurado-Ruiz, F., Rousseau, D., Botía, J. A., & Aranzana, M. J. (2023). GenoDrawing: An autoencoder framework for image prediction from SNP markers. Plant Phenomics, 5, 0113. https://doi.org/10.34133/plantphenomics.0113
Bouhlel, Nizar and Rousseau, David (2023). Exact Rényi and Kullback-Leibler Divergences Between Multivariate t-Distributions, IEEE Signal Processing Letters. https://doi.org/10.1109/LSP.2023.3324594
El Ghaziri A, Bouhlel N, Sapoukhina N, Rousseau D. On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging. Remote Sensing. 2023; 15(2):528. https://doi.org/10.3390/rs15020528
2022
Sapoukhina N, Boureau T and Rousseau D (2022). Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset. Front. Plant Sci. 13:969205. doi: 10.3389/fpls.2022.969205
Turgut, K., Dutagaci, H. and Rousseau, D. (2022). RoseSegNet: An attention-based deep learning architecture for organ segmentation of plants. Biosystems Engineering, 221, 138-153. doi: 10.1016/j.biosystemseng.2022.06.016
Barrit, T., Campion, C., Aligon, S., Bourbeillon, J., Rousseau, D., Planchet, E. and Teulat, B. (2022). A new in vitro monitoring system reveals a specific influence of Arabidopsis nitrogen nutrition on its susceptibility to Alternaria brassicicola at the seedling stage. Plant Methods, 2022, 18 (1), pp.131. doi: 10.1186/s13007-022-00962-3
Bouhlel, N., Akbari, V., Méric, S. and Rousseau, D. (2022). Multivariate Statistical Modeling for Multitemporal SAR Change Detection Using Wavelet Transforms and Integrating Subband Dependencies. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-18, 2022, Art no. 5237018, doi: 10.1109/TGRS.2022.3215783.
Bouhlel, N., and Rousseau, D. (2022). A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions. Entropy, 24(6), 838. https://doi.org/10.3390/e24060838
Ahmad, A., Sala, F., Paiè, P., Candeo, A., D'Annunzio, S., Zippo, A., ... and Rousseau, D. (2022). On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy. Lab on a Chip, 22(18), 3453-3463. doi: 10.1039/D2LC00482H
Turgut, K., Dutagaci, H., Galopin, G. and Rousseau, D. (2022). Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods. Plant Methods, 18(1), 1-23. doi: 10.1186/s13007-022-00857-3
Mohammad-Razdari, A., Rousseau, D., Bakhshipour, A., Taylor, S., Poveda, J., and Kiani, H. (2022). Recent advances in E-monitoring of plant diseases. Biosensors and Bioelectronics, 113953. doi: 10.1016/j.bios.2021.113953
Rayan Eid, Claudine Landès, Alix Pernet, Emmanuel Benoît, Pierre Santagostini, Angélina El Ghaziri and Julie Bourbeillon. DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets. BioData Mining, BioMed Central, 2022, 15 (1), pp.10. ⟨10.1186/s13040-022-00293-y⟩. doi: 10.1186/s13040-022-00293-y
Osei-Kwarteng, M., Ayipio, E., Moualeu-Ngangue, D., Buck-Sorlin, G. and Stützel, H. (2022). Interspecific variation in leaf traits, photosynthetic light response, and whole-plant productivity in amaranths (Amaranthus spp. L.). PloS one, 17(6), e0270674. doi: 10.1371/journal.pone.0270674
2021
Baleghi, Y., and Rousseau, D. (2021). An analytical proof on suitability of Cauchy-Schwarz Divergence as the aggregation criterion in Region Growing Algorithm. Image and Vision Computing, 104312. doi: 10.1016/j.imavis.2021.104312
Douarre, C., Crispim-Junior, C. F., Gelibert, A., Germain, G., Tougne, L., and Rousseau, D. (2021). CTIS-Net: a neural network architecture for compressed learning based on Computed Tomography Imaging Spectrometers. IEEE Transactions on Computational Imaging. doi: 10.1109/TCI.2021.3083215
ElMasry, G., Mandour, N., Ejeez, H., Demilly, D., Al-Rejaie, S., Verdier, J. and Rousseau, D. (2021). Multichannel imaging for monitoring chemical composition and germination capacity of cowpea (Vigna unguiculata) seeds during development and maturation. The Crop Journal. doi: 10.1016/j.cj.2021.04.010
Debs, N., Cho, T. H., Rousseau, D., Berthezène, Y., Buisson, M., Eker, O. and Frindel, C. (2021). Impact of the reperfusion status for predicting the final stroke infarct using deep learning. NeuroImage: Clinical, 29, 102548. doi: 10.1016/j.nicl.2020.102548
Chéné, Y., Belin, É., Coadou, F., Chapeau-Blondeau, F., Hardouin, L., and Rousseau, D. (2021). Instrumentation et capteurs innovants appliqués au phénotypage automatisé des végétaux. In Instrumentation et Interdisciplinarité (pp. 239-244). EDP Sciences. doi: 10.1051/978-2-7598-1206-6-030
Zhang, Y., Henke, M., Buck-Sorlin G.H., Li Y., Xu H., Liu X. and Li T. (2021). Estimating canopy leaf physiology of tomato plants grown in a solar greenhouse: Evidence from simulations of light and thermal microclimate using a Functional-Structural Plant Model. Agricultural and Forest Meteorology, 307, 108494. doi: 10.1016/j.agrformet.2021.108494
Ramananjatovo, T., Chantoiseau, E., Guillermin, P., Guénon, R., Delaire, M., Buck-Sorlin, G.H. and Cannavo, P. (2021). Growth of Vegetables in an Agroecological Garden-Orchard System: The Role of Spatiotemporal Variations of Microclimatic Conditions and Soil Properties. Agronomy, 11(9), 1888. doi: 10.3390/agronomy11091888
Ramananjatovo, T., Chantoiseau, E., Buck-Sorlin, GH., Guillermin, P., Guénon, R., Delaire, M. and Cannavo, P. (2021). Microclimatic conditions affect lettuce growth in apple tree-lettuce intercropping. Acta Hortic. 1327, 237-244; DOI: 10.17660/ActaHortic.2021.1327.31; https://doi.org/10.17660/ActaHortic.2021.1327.31
Beroueg A, Buck-Sorlin GH, Couvreur V, Danjon F, Delory BM, et al.. (2021). Loïc Pagès, founding scientist in root ecology and modelling. in silico Plants, Oxford Academic, 3 (2), ⟨10.1093/insilicoplants/diab035⟩. doi: 10.1093/insilicoplants/diab035
Bourbeillon, J., Coisnon, T., Rousselière, D. and Salanié, J. Characterising the Landscape in the Analysis of Urbanisation Factors: Methodology and Illustration for the Urban Area of Angers. Economie et Statistique / Economics and Statistics, INSEE, 2021, 528-529, pp.109 - 128. doi: 10.24187/ecostat.2021.528d.2062
Boumaza, R., Santagostini, P., Yousfi, S. and Demotes-Mainard, S. (2021). dad: an R Package for Visualisation, Classification and Discrimination of Multivariate Groups Modelled by their Densities. The R Journal (2021) 13:2, pages 179-207. doi: 10.32614/RJ-2021-071
2020
Zine-El-Abidine, M., Dutagaci, H., Galopin, G. and Rousseau, D. (2020). Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point Clouds.Biosystem engineering. doi: 10.1016/j.biosystemseng.2021.06.015
Douarre, C., Crispim-Junior, C. F., Gelibert, A., Tougne, L. and Rousseau, D. (2020). On the value of CTIS imagery for neural-network-based classification: a simulation perspective. Applied optics, 59(28), 8697-8710. doi: 10.1364/AO.394868
Samiei, S., Rasti, P., Richard, P., Galopin, G. and Rousseau, D. (2020). Toward Joint Acquisition-Annotation of Images with Egocentric Devices for a Lower-Cost Machine Learning Application to Apple Detection. Sensors, 20(15), 4173. doi: 10.3390/s20154173
Samiei, S., Rasti, P., Vu, J. L., Buitink, J. and Rousseau, D. (2020). Deep learning-based detection of seedling development. Plant Methods, 16(1), 1-11. doi: 10.1186/s13007-020-00647-9
Garbez, M., Belin, E., Chéné, Y., Dones, N., Hunault, G., Relion, D., Rousseau D. and Galopin, G. (2020). A new approach to predict the visual appearance of rose bush from image analysis of 3D videos. Eur. J. Hortic. Sci, 85, 182-190. doi: 10.17660/eJHS.2020/85.3.6
Xu, L., Yang, Z., Ding, W., and Buck-Sorlin, G.H. (2020). Physics-based algorithm to simulate tree dynamics under wind load. International Journal of Agricultural and Biological Engineering, 13(2), 26-32. doi: 10.25165/j.ijabe.20201302.4967
Langensiepen, M., Jansen, M. A., Wingler, A., Demmig-Adams, B., Adams III, W.W., Dodd, I.C., ... and Buck-Sorlin, G.H. and Munné-Bosch, S. (2020). Linking integrative plant physiology with agronomy to sustain future plant production. Environmental and experimental botany, 178, 104125. doi: 10.1016/j.envexpbot.2020.104125
Wang, W., Celton, J. M., Buck-Sorlin, GH., Balzergue, S., Bucher, E. and Laurens, F. (2020). Skin Color in Apple Fruit (Malus× domestica): Genetic and Epigenetic Insights. Epigenomes, 4(3), 13. doi: 10.1016/j.envexpbot.2020.104125
Chapeau-Blondeau, F. and Belin, E. (2020). Fourier-transform quantum phase estimation with quantum phase noise. Signal Processing, 170, 107441. doi: 10.1016/j.sigpro.2019.107441
Leclerc, P., Ray, C., Mahieu-Williame, L., Alston, L., Frindel, C., Brevet, P. F. and Rousseau, D. (2020). Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. Scientific reports, 10(1), 1-9. doi: 10.1038/s41598-020-58299-7
Méline, V., Brin, C., Lebreton, G., Ledroit, L., Sochard, D., Hunault, G., ... and Belin, E. (2020). A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors. Frontiers in Plant Science, 11, 213. doi: 10.3389/fpls.2020.00213
Debs, N., Rasti, P., Victor, L., Cho, T. H., Frindel, C. and Rousseau, D. (2020). Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Computers in Biology and Medicine, 116, 103579. doi: 10.1016/j.compbiomed.2019.103579
Ahmad, A., Frindel, C. and Rousseau, D. (2020). Detecting differences of fluorescent markers distribution in single cell microscopy: textural or pointillist feature space? Frontiers in Robotics and AI, 7, 39. doi: 10.3389/frobt.2020.00039
Dutagaci, H., Rasti, P., Galopin, G. and Rousseau, D. (2020). ROSE-X: an annotated data set for evaluation of 3D plant organ segmentation methods. Plant methods, 16(1), 1-14. doi: 10.1186/s13007-020-00573-w
ElMasry, G., ElGamal, R., Mandour, N., Gou, P., Al-Rejaie, S., Belin, E. and Rousseau, D. (2020). Emerging Thermal Imaging Techniques for Seed Quality Evaluation: Principles and Applications. Food Research International, 109025. doi: 10.1016/j.foodres.2020.109025
2019
Desgeorges, T., Liot, S., Lyon, S., Bouviere, J., Kemmel, A., Trignol, A., Rousseau D. and Chazaud, B. (2019). Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle. Skeletal muscle, 9(1), 2. doi: 10.1186/s13395-018-0186-6
Rasti, P., Wolf, C., Dorez, H., Sablong, R., Moussata, D., Samiei, S. and Rousseau, D. (2019). Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy. Scientific Reports, 9(1), 1-11. doi: 10.1038/s41598-019-56583-9
Rasti, P., Ahmad, A., Samiei, S., Belin, E. and Rousseau, D. (2019). Supervised image classification by scattering transform with application to weed detection in culture crops of high density. Remote Sensing, 11(3), 249. doi: 10.3390/rs11030249
ElMasry, G., Mandour, N., Wagner, M. H., Demilly, D., Verdier, J., Belin, E. and Rousseau, D. (2019). Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds. Plant methods, 15(1), 24. doi: 10.1186/s13007-019-0411-2
Gillard, N., Belin, É. and Chapeau-Blondeau, F. (2019). Stochastic resonance with unital quantum noise. Fluctuation and Noise Letters, 18(03), 1950015. doi: 10.1142/S0219477519500159
Douarre, C., Crispim-Junior, C. F., Gelibert, A., Tougne, L. and Rousseau, D. (2019). Novel data augmentation strategies to boost supervised segmentation of plant disease. Computers and Electronics in Agriculture, 165, 104967. doi: 10.1016/j.compag.2019.104967
Alston, L., Mahieu-Williame, L., Hebert, M., Kantapareddy, P., Meyronet, D., Rousseau, D. and Montcel, B. (2019). Spectral complexity of 5-ALA induced PpIX fluorescence in guided surgery: a clinical study towards the discrimination of healthy tissue and margin boundaries in high and low grade gliomas. Biomedical optics express, 10(5), 2478-2492. doi: 10.1364/BOE.10.002478
Sdika, M., Alston, L., Rousseau, D., Guyotat, J., Mahieu-Williame, L. and Montcel, B. (2019). Repetitive motion compensation for real time intraoperative video processing. Medical image analysis, 53, 1-10. doi: 10.1016/j.media.2018.12.005
ElMasry, G., Mandour, N., Al-Rejaie, S., Belin, E. and Rousseau, D. (2019). Recent applications of multispectral imaging in seed phenotyping and quality monitoring—An overview. Sensors, 19(5), 1090. doi: 10.3390/s19051090
2018
Samiei, S., Rasti, P., Daniel, H., Belin, E., Richard, P. and Rousseau, D. (2018). Toward a Computer Vision Perspective on the Visual Impact of Vegetation in Symmetries of Urban Environments. Symmetry, 10(12), 666. doi: 10.3390/sym10120666
Zweifel, S., Buquet, J., Caruso, L., Rousseau, D.and Raineteau, O. (2018). “FlashMap” - A Semi-Automatic Tool for Rapid and Accurate Spatial Analysis of Marker Expression in the Subventricular Zone. Scientific reports, 8(1), 1-13. doi: 10.1038/s41598-018-33939-1
Zondaka, Z., Harjo, M., Khorram, M. S., Rasti, P., Tamm, T. and Kiefer, R. (2018). Polypyrrole/carbide-derived carbon composite in organic electrolyte: Characterization as a linear actuator. Reactive and Functional Polymers, 131, 414-419. doi: 10.1016/j.reactfunctpolym.2018.08.020
Douma, I., Rousseau, D., Sallit, R., Kodjikian, L. and Denis, P. (2018). Toward quantitative and reproducible clinical use of OCT-Angiography. PloS one, 13(7). doi: 10.1371/journal.pone.0197588
Gillard, N., Belin, E. and Chapeau-Blondeau, F. (2018). Enhancing qubit information with quantum thermal noise. Physica A: Statistical Mechanics and its Applications, 507, 219-230. doi: 10.1016/j.physa.2018.05.099
Giacalone, M., Rasti, P., Debs, N., Frindel, C., Cho, T. H., Grenier, E. and Rousseau, D. (2018). Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Medical image analysis, 50, 117-126. doi: 10.1016/j.media.2018.08.008
Garbez, M., Symoneaux, R., Belin, É., Caraglio, Y., Chéné, Y., Dones, N., ... and Rousseau, D. (2018). Ornamental plants architectural characteristics in relation to visual sensory attributes: a new approach on the rose bush for objective evaluation of the visual quality. Eur.J.Hortic.Sci. 83 (3) 187-201. doi: 10.17660/eJHS.2018/83.3.8
Murtin, C., Frindel, C., Rousseau, D. and Ito, K. (2018). Image processing for precise three-dimensional registration and stitching of thick high-resolution laser-scanning microscopy image stacks. Computers in biology and medicine, 92, 22-41. doi: 10.1016/j.compbiomed.2017.10.027
Chambon, A., Boureau, T., Lardeux, F. and Saubion, F. (2018). Logical characterization of groups of data: a comparative study. Applied Intelligence, 48(8), 2284-2303. doi: 10.1007/s10489-017-1080-3
Denancé, Nicolas, et al. Two ancestral genes shaped the Xanthomonas campestris TAL effector gene repertoire. New Phytologist 219.1 (2018): 391-407. doi: 10.1111/nph.15148
Alston, L., Rousseau, D., Hébert, M., Mahieu-Williame, L. and Montcel, B. (2018). Nonlinear relation between concentration and fluorescence emission of protoporphyrin IX in calibrated phantoms. Journal of biomedical optics, 23(9), 097002. doi: 10.1117/1.JBO.23.9.097002
Courtial, Julia, et al. Aldaulactone–an original phytotoxic secondary metabolite involved in the aggressiveness of Alternaria dauci on carrot. Frontiers in plant science 9 (2018): 502. doi: 10.3389/fpls.2018.00502
2017
Gillard, N., Belin, E. and Chapeau-Blondeau, F. (2017). Qubit state detection and enhancement by quantum thermal noise. Electronics Letters, 54(1), 38-39. doi: 10.1049/el.2017.2233
Gillard, N., Belin, E. and Chapeau-Blondeau, F. (2017). Stochastic antiresonance in qubit phase estimation with quantum thermal noise. Physics Letters A, 381(32), 2621-2628. doi: 10.1016/j.physleta.2017.06.009
Chapitres d'ouvrages
2024
Cordier, M., Rasti, P., Torres, C. and Rousseau, D. Network of Low-Cost RGB-Depth Cameras and Mini-Computers for High-Throughput Monitoring of Plant Stress Response. In Plant Speed Breeding and High-throughput Technologies (pp. 168-186). CRC Press.
2023
Scalisi, S., Ahmad, A., D’Annunzio, S., Rousseau, D. and Zippo, A. (2023). Quantitative Analysis of PcG-Associated Condensates by Stochastic Optical Reconstruction Microscopy (STORM). In Polycomb Group Proteins: Methods and Protocols (pp. 183-200). New York, NY: Springer US.
2021
Hamdy, S., Rasti, P., Charrier, A. and Rousseau, D. (2021). Advances in seed phenotyping and applications to seed testing/monitoring and breeding ; Focus on seed phenotyping with X-Ray imaging (to appear 2021).
Belin, E. and Rousseau, D. (2021). Biospeckle Imaging; A compendium of imaging modalities for biological and preclinicial research (IOP 2021).
Belin, E. and Rousseau, D. (2021). Biospeckle imaging. Imaging Modalities for Biological and Preclinical Research: A Compendium, 1, I-8, ISBN: 978-0-7503-3059-6. IOP ebooks. Bristol, UK: IOP Publishing. [ https://ui.adsabs.harvard.edu/link_gateway/2021imb1.book...36R/doi:10.1088/978-0-7503-3059-6ch36 | 10.1088/978-0-7503-3059-6ch36 ]
2020
Chapeau-Blondeau, F. and Belin, E. (2020). Quantum signal processing for quantum phase estimation: Fourier transform versus maximum likelihood approaches, Annals of Telecommunications - annales des télécommunications, Springer, 2020, 75 (11-12), pp.641-653.
Interventions dans des conférences /congrès et séminaires de recherche
2024
Mercier, F., Bouhlel, N., El Ghaziri, A. and Rousseau, D. (2024). Online Bayesian Adaptive Sampling for Nonlinear Model: Application to Plant Phenotyping. 32nd European Signal Processing Conference (EUSIPCO 2024), août 26-30, 2024, Lyon, France.
Bdiri, W., Bouhlel, N., Méric, S., Pottier, E. and Kallel, F. (2024). Unsupervised Classification of Polarimetric SAR Images Using Bayesian Nonparametric Model and Markov Random Field. 32nd European Signal Processing Conference (EUSIPCO 2024), août 26-30, 2024, Lyon, France.
Brechet, B., Gilet, V., Loumaigne, M., Bouhlel, N., Mabilleau, G. and Rousseau, D. (2024). On the Use of Spatial and Spectral Redundancy to Speed-Up Brillouin Micro-Imaging. 32nd European Signal Processing Conference (EUSIPCO 2024), août 26-30, 2024, Lyon, France.
Akbari, V., Bouhlel, N. and Méric, S. (2024). Advanced Statistical Modelling of Polarimetric SAR Data for Land Cover Change Detection Analysis. Oral presentation in the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024), 7-12 July, 2024, Athens, Greece.
Bdiri, W., Bouhlel, N., Méric, S., Pottier, E. and Kallel, F. (2024). A Bayesian Non-parametric Model for Unsupervised Change Detection of Fully Polarimetric SAR Images. Oral presentation in the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024), 7-12 July, 2024, Athens, Greece.
Santagostini, P. and Bouhlel, N. (2024). Distance/Divergence entre distributions t multivariées. Rencontres R 2024, 12-14 juin 2024, Vannes, France. https://rr2024.sciencesconf.org/543724
Zine-El-Abidine, M., Dutagaci, H., Rasti, P., Aranzana, M., Dujak, C. and Rousseau, D. (2024, May). Toward Objective Variety Testing Score Based on Computer Vision and Unsupervised Machine Learning: Application to Apple Shape. In 4th International Conference on Image Processing and Vision Engineering (pp. 15-22). SCITEPRESS-Science and Technology Publications. http://dx.doi.org/10.5220/0012549700003720
2023
Gilet, V., Mabilleau, G., Loumaigne, M., Coic, L., Vitale, R., Oberlin, T., ... and Rousseau, D. (2023). Superpixels meet essential spectra for fast Raman hyperspectral microimaging. Optics Express, 32(1), 932-948. doi: 10.1364/OE.509736
Bouhlel, N., Mercier, F., El Ghaziri, A. and Rousseau, D. (2023). Parameter Estimation of the Normal Ratio Distribution with Variational Inference. 31th European Signal Processing Conference (EUSIPCO), 4-8 September 2023, Helsinki, Finlande. http://dx.doi.org/10.23919/EUSIPCO58844.2023.10290111
Santagostini, P. and Bouhlel, N. (2023). Packages mggd et mcauchyd – Distribution gaussienne généralisée multivariée, distribution de Cauchy multivariée. 9 èmes Rencontres R, 21-23 juin 2023, Avignon, France. https://rr2023.sciencesconf.org/465678
Akbari, V. and Bouhlel, N. (2023). Change Detection in Multilook Polarimetric SAR Imagery With Hoteling Lawley Trace and Determinant Ratio Test Statistics. 11th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry and BIOMASS Workshop, 19-23 June 2023, espaces Vanel Toulouse, France. https://polinsar-biomass2023.esa.int/
2022
Bouhlel, N. and Rousseau, D. (2022). Multi-Temporal SAR Change Detection using Wavelet Transforms. 2022 30th European Signal Processing Conference (EUSIPCO), 2022, pp. 538-542, Belgrade, Serbia, https://ieeexplore.ieee.org/document/9909568
Bouhlel, N., Mercier, F. and Rousseau, D. (2022). Détection de changement dans les images SAR polarimétriques hétérogènes. 28ième GRETSI, 6-9 septembre 2022, Nancy, France.
Bouillon, P., Fanciullino, A.L., Balzergue, S., Hanteville, S. Belin, E., et al. (2022). Development and comparison of phenotypic methods for colour assessment and polyphenolic composition evaluation in red flesh apples. In IHC 2022 31st International Horticultural Congress, aug. 2022. Angers, France.
Pernet, A., Eid, R., Landès, C., Benoît, E., Santagostini, P. et al.. Construction of a semantic distance for inferring structure of the variability between 19th century Rosa varieties. IHC 2022 31st International Horticultural Congress, Aug 2022, Angers, France. ⟨hal-03823016⟩.
Buck-Sorlin, G.H., Tavkhelidze, A. and Kurth, W. (2022). A model of water and carbohydrate transport in fruit-bearing apple tree branches: effect of pruning-induced modifications in architecture. International symposium on innovative perennial crops management, International Horticultural Congress 2022, Angers, 14 – 19 August, 2022.
Buck-Sorlin, G.H., Bournet, P.-E., Rossdeutsch, L., Truffault, V. (2022). Optimizing photosynthetic activity of high-wire cucumber production systems using a functional-structural plant modelling approach. International symposium on innovative technologies and production strategies for sustainable controlled environment horticulture, International Horticultural Congress 2022, Angers, 14 – 19 August, 2022.
Domingo Molina Aiz, F., Buck-Sorlin, G.H., Marcelis, L.F.M., Fatnassi, F. (2022). How can plant modelling be a leverage for cropping system improvement by integrating plant physiology and smart horticulture? Workshop W5, International Horticultural Congress 2022, Angers, 14 – 19 August, 2022.
Bourbeillon, J., Céline, M., Alice, M. and Vandenkoornhuyse, C. (2022). En quoi l'accompagnement des élèves facilite-t-il leur engagement dans le cadre d'un travail collaboratif en mode hybride ? L'exemple d'un Wiki collaboratif. 32ème Congrès de l’Association Internationale de Pédagogie Universitaire, May 2022, Rennes, France.
2021
ElMasry, G., ElGamal, R., Mandour, N., Al-Rejaie, S., Belin, E. and Rousseau, D. (2021). Thermal imaging applications in seed quality evaluation, 13th International Conference on Agrophysics: Agriculture in changing climate, nov 2021, Lublin, Poland.
ElMasry, G., Mandour, N., Morsy, N., ElKhouly, D., Al-Rejaie, S., Belin, E. and Rousseau, D. (2021). High throughput phenotyping of cowpea seeds during developmental stages using multichannel imaging, 13th International Conference on Agrophysics: Agriculture in changing climate, nov 2021, Lublin, Poland.
Redon, M., Boureau, T., Rousseau, D. and Belin, E. (2021). Approches d’active learning appliquées aux données du système robotisé de phénotypage Phenobean. Journée d’animation scientifique de l’axe ASM Biogenouest, 2021, Angers, France, 2021.
Santagostini, P. and El Ghaziri, A. . linmodel – Un package fournissant une application shiny pour les modèles linéaires et les tests non paramétriques. Rencontres R 2021, Paris, France.
2020
Belin, E. and Boureau, T. (2020). Phenotic : plate-forme d’imagerie pour semences et plantes. Journée d’animation Imabio, Angers, France, 2020.
Belin, E., Gardet, R., Demilly, D. and Boureau, T. (2020). L’imagerie au service de l’évaluation de la qualité des semences et plantules, Congrès Gen2bio du réseau Biogenouest, 2020.
Belin, E., Gardet, R. and Boureau, T. (2020). Phénotypage à haut-débit des stress biotiques sur les parties aériennes des plantes. Congrès Gen2bio du réseau Biogenouest, 2020.
Hauguel, L., Lallemand, T., Eid, R., Dupuis, F., Gaillard, S., et al. (2020). ELTerm: a terminology module for a plant data management system. Journée Ouvertes de Biologie, Informatique, Mathématiques, JOBIM 2020, Jun 2020, Montpellier (virtuel), France.
2019
Rousseau, D. and Tasftaris, S. (2019) Data augmentation techniques for deep learning: A tutorial. ICASSP, 2019, Brighton, United Kingdom
Sapoukhina, N., Samiei, S., Rasti, P. and Rousseau, D. (2019) Data augmentation from RGB to chlorophyll fluorescence imaging Application to leaf segmentation of Arabidopsis thaliana from top view images. CVPR, 2019, Long Beach, États-Unis
Debs, N., Decroocq, M., Cho, T.H., Rousseau, D. and Frindel, C. (2019). Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network. MICCAI, 2019, Shenzhen, China
Rousseau, D. (2019). Lowering the cost of spectral imaging and machine learning: Application to plant disease detection Chemo 2019.
Garbouge, H., Santagostini, P., Charrier, A., Demilly, D. and Rousseau, D. (2019). Ordinal clustering of seed populations with data extracted from RGB imaging and X-ray tomography. UseR conference, 2019, Toulouse, France
Douarre, C., Tougne, L., Crispim-Junior, C., Gelibert, A. and Rousseau, D. (2019). When spectro-imaging meets machine learning. Workshop on Machine Learning Assisted Image Formation, Jul 2019, Nice, France
Dutagaci, H., Belin, E. and Rousseau, D. (2019). Revisiting SIFT for plant foliage in RGB images acquired on a turntable. 7th International Workshop on Image Analysis Methods for the Plant Sciences, Jul 2019, Lyon, France
Douarre, C., Crispim-Junior, C., Gelibert, A., Rousseau, D. and Tougne, L. (2019). A strategy for multimodal canopy images registration. 7th International Workshop on Image Analysis Methods in the Plant Sciences, Jul 2019, Lyon, France
Ahmad, A., Frindel, C., Rasti, P., Sarrut, D. and Rousseau, D. (2019). Deep learning based detection of cells in 3D light sheet fluorescence microscopy Quantitative BioImaging Conference (QBI 2019), 2019, Rennes, France
Parisse, N., Gourrier, A., Genthial, R., Débarre, D., Bassi, A. and Rousseau, D. (2019). Graph encoding of multiscale structural networks from binary images with application to bio imaging
2018
Gillard, N., Belin, E. and Chapeau-Blondeau, F. (2018). Digital image processing with quantum approaches. 8th International Conference on Image and Signal Processing, ICISP 2018., 2018, Cherbourg, France. pp.360-369
Rasti, P., Demilly, D., Benoit, L., Belin, E., Ducournau, S., Chapeau-Blondeau, F. and Rousseau, D. (2018) Low-cost vision machine for high-throughput automated monitoring of heterotrophic seedling growth on wet paper support. Computer Vision Problems in Plant Phenotyping (CVPPP 2018), 2018, Newcastle, United Kingdom
Ahmad, A., Guyonneau, R., Mercier, F. and Belin, E. (2018). An Image Processing Method Based on Features Selection for Crop Plants and Weeds Discrimination Using RGB Images. International Conference on Image and Signal Processing, ICISP 2018, 2018, Cherbourg, France. pp.3-10
Parisse, N., Gourrier, A., Genthial, R., Débarre, D., Bassi, A. and Rousseau, D. (2018). Computer Vision Problems in Plant Phenotyping (CVPPP 2018), Sep 2018, Newcastle, United Kingdom
Debs, N., Giacalone, M., Rasti, P., Cho, T.H., Frindel, C. and Rousseau, D. (2018). Perfusion MRI in stroke as a regional spatio-temporal texture. ISMRM 27th Annual Meeting & Exhibition, Jun 2018, Paris, France
Jacquesson, T., Bosc, J., Rositi, H., Wiart, M., Chauveau, F., Peyrin, F., Rousseau, D. and Frindel, C. (2018). Synchrotron X-Ray Phase-Contrast Imaging To Simulate Diffusion Tensor MRI: Application to Tractograhy Joint Annual Meeting ISMRM-ESMRMB 2018, 2018, Non spécifié, France
Rasti, P., Ahmad, A., Belin, E. and Rousseau, D. (2018). Learning on Deep Network without the Hot Air by Scattering Transform Application to Weed Detection in Dense Culture. 7th International Workshop on Image Analysis for Plant Science (IAMPS), 2018, Nottingham, United Kingdom
2017
Bujoreanu, D., Rasti, P., Rousseau D. (2017). On the value of graph-based segmentation for the analysis of structural networks in life sciences. 25th European Signal Processing Conference (EUSIPCO), 2017, Kos, Greece. pp.2664-2668
Outils et produits électroniques
Logiciels
2024
Metuarea, H., & Rousseau, D. (2024, February). Toward more collaborative deep learning project management in plant phenotyping. In 2024 NAPPN Annual Conference. https://www.napari-hub.org/plugins/manini
Santagostini P, Bouhlel N (2024). mstudentd: Multivariate t Distribution. R package version 1.0.0, https://CRAN.R-project.org/package=mstudentd. doi: 10.32614/CRAN.package.dad
2023
Napari Blossom : https://github.com/hereariim/napari-blossom
Napari Apple : https://github.com/hereariim/napari-apple
Napari indices : https://www.napari-hub.org/plugins/napari-indices
Zine-El-Abidine, M., Dutagaci, H., & Rousseau, D. (2023). Ordinalysis: Interpretability of multidimensional ordinal data. SoftwareX, 22, 101343
Couasnet, G., Cordier, M., Garbouge, H., Mercier, F., Pierre, D., El Ghaziri, A., Rasti, P. & Rousseau, D. (2023). Growth Data—An automatic solution for seedling growth analysis via RGB-Depth imaging sensors. SoftwareX, 24, 101572
Santagostini P, Bouhlel N (2023). mcauchyd: Multivariate Cauchy Distribution; Kullback-Leibler Divergence. R package version 1.2.0, https://CRAN.R-project.org/package=mcauchyd. doi: 10.32614/CRAN.package.mcauchyd
2022
Santagostini P, Bouhlel N (2022). mggd: Multivariate Generalised Gaussian Distribution; Kullback-Leibler Divergence. R package version 1.2.3, https://CRAN.R-project.org/package=mggd. doi: 10.32614/CRAN.package.mggd
2021
Ahmad, A., Vanel, G., Camarasu-Pop, S., Bonnet, A., Frindel, C., & Rousseau, D. (2021). MicroVIP: microscopy image simulation on the virtual imaging platform. SoftwareX, 16, 100854. doi: 10.1016/j.softx.2021.100854
Boumaza R, Santagostini P, Yousfi S, Hunault G, Bourbeillon J, Pumo B, Demotes-Mainard S (2021). dad: Three-Way / Multigroup Data Analysis Through Densities. R package version 4.0.0, https://CRAN.R-project.org/package=dad.
2018
David Rousseau in the framework of industrial partnership with ZEISS enabled to boost significantly the sells of microscope Z1 while speeding up registration of images of 100 Go from several hours to some minutes. Fiji plugin published in Computers in Medicine and Biology 2018.
Bases de données
Rousseau, D. and Rasti, P. Annotated data set on colon cancer
Rousseau, D. and Rasti, Organisation of AgTech Data challenge, first national data challenge organized on AgTech
Instruments et méthodologies
Prototypes
Rousseau, D. and Rasti, P.: ANR LABCOM ESTIM (2017-2020) networks of depth imaging camera for the monitoring of 300 000 seedling delivered to AREXOR
PHENOTIC Platform labeled BIOGENOUEST, IBISA member of national infrastructure PHENOME
Autres produits
Activités éditoriales
2024
Zhou, J., Rousseau, D., Mueller-Linow, M., Lube, V. and Eves-van den Akker, S. (2024). Affordable Phenotyping to Enable Desirable Discoveries in Plant Research. Plant Phenomics. https://spj.science.org/page/plantphenomics/si/affordable-phenotyping-enable-desirable-discoveries-plant-research
Shichao, J., Rousseau, D., Jimenez-Berni, J.A. and Cen, H. (2024). Affordable Phenotyping to Enable Desirable Discoveries in Plant Research. Plant Phenomics. https://spj.science.org/page/plantphenomics/si/3d-plant-phenomics
2021
Belin, E. and Rousseau, D. (2021). Biospeckle imaging. Imaging Modalities for Biological and Preclinical Research: A Compendium, 1, I-8, ISBN: 978-0-7503-3059-6. IOP ebooks. Bristol, UK: IOP Publishing. https://ui.adsabs.harvard.edu/link_gateway/2021imb1.book...36R/doi:10.1088/978-0-7503-3059-6ch36
2020
Special issue on Low-Cost Sensors and Vectors for Plant Phenotyping.https://www.mdpi.com/journal/sensors/special_issues/sensors_vectors_plant_phenotyping
