Barbara Pascal

Barbara Pascal

I am currently a CNRS Researcher in the LS2N, in Nantes, France. Before that I was a postdoctoral researcher at the CRIStAL for two years, taking part in the ERC project of R. Bardenet. I received the degree of Professeur-Agrégé of Mathematics in 2016 at École Normale Supérieure de Lyon, then completed my M.S. degree in Physics from the ENS Lyon, France, in 2017, and received the Ph.D degree in Signal & Image Processing from the ENSL in 2020, for my work at the Laboratoire de Physique, under the supervision of P. Abry and N. Pustelnik.

Scholar

Orcid

Arxiv

Preprints

  1. Pascal, B., & Bardenet, R. (2024). Point Processes and spatial statistics in time-frequency analysis.
    paper arXiv
  2. Du, J., Pascal, B., & Abry, P. (2024). Synthetic Spatiotemporal Covid19 Infection Counts to Assess Graph-Regularized Estimation of Multivariate Reproduction Numbers.
    paper
  3. Pascal, B., & Lagrange, M. (2024). On the Robustness of Musical Timbre Perception Models: From Perceptual to Learned Approaches.
    paper

Journal articles

  1. Lucas, C.-G., Pascal, B., Pustelnik, N., & Abry, P. (2023). Hyperparameter selection for Discrete Mumford–Shah. Signal, Image and Video Processing, 17(5), 1897–1904.
    paper arXiv
  2. Fort, G., Pascal, B., Abry, P., & Pustelnik, N. (2023). Covid19 reproduction number: Credibility intervals by blockwise proximal Monte Marlo samplers. IEEE Transactions on Signal Processing.
    paper arXiv
  3. Pascal, B., & Bardenet, R. (2022). A covariant, discrete time-frequency representation tailored for zero-based signal detection. IEEE Transactions on Signal Processing, 70, 2950–2961.
    paper arXiv code
  4. Pascal, B., Abry, P., Pustelnik, N., Roux, S., Gribonval, R., & Flandrin, P. (2022). Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data. IEEE Transactions on Signal Processing, 70, 2859–2868.
    paper arXiv
  5. Pascal, B., Pustelnik, N., & Abry, P. (2021). Strongly Convex Optimization for Joint Fractal Feature Estimation and Texture Segmentation. Applied and Computational Harmonic Analysis, 54, 303–322.
    paper arXiv
  6. Pascal, B., Vaiter, S., Pustelnik, N., & Abry, P. (2021). Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. J. Math. Imaging Vis., 1–30.
    paper arXiv code
  7. Pascal, B., Pustelnik, N., Abry, P., J.-C., G., & Vidal, V. (2020). Parameter-free and fast nonlinear piecewise filtering: application to experimental physics. Ann. Telecommun., 75(11), 655–671.
    arXiv code

Conference papers

  1. Du, J., Pascal, B., & Abry, P. (2023, August). Compared performance of Covid19 reproduction number estimators based on realistic synthetic data. GRETSI’23 XXIXème Colloque Francophone De Traitement Du Signal Et Des Images.
    paper
  2. Abry, P., Fort, G., Pascal, B., & Pustelnik, N. (2023, September). Credibility intervals for the reproduction number of the Covid-19 pandemic using proximal Langevin samplers. EUSIPCO 2023.
    paper video slides
  3. Abry, P., Chevallier, J., Fort, G., & Pascal, B. (2023, December). Pandemic Intensity Estimation from Stochastic Approximation-based Algorithms. 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.
    paper
  4. Le, H. T. V., Pascal, B., Pustelnik, N., Foare, M., & Abry, P. (2022, September). Algorithmes proximaux rapides déroulés pour l’analyse d’images fractales homogènes par morceaux. GRETSI’22 XXVIIIème Colloque Francophone De Traitement Du Signal Et Des Images.
    paper
  5. Pascal, B., & Bardenet, R. (2022, September). Une famille de représentations covariantes de signaux discrets et son application à la détection de signaux à partir de leurs zéros. GRETSI’22 XXVIIIème Colloque Francophone De Traitement Du Signal Et Des Images.
    paper
  6. Abry, P., Fort, G., Pascal, B., & Pustelnik, N. (2022, July). Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling. 44th Annual International Conference of IEEE Eng. Med. Biol. Soc.
    paper
  7. Artigas, H., Pascal, B., Fort, G., Abry, P., & Pustelnik, N. (2022, August). Credibility interval design for COVID19 reproduction number from nonsmooth Langevin-type Monte Carlo sampling. EUSIPCO 2022.
    paper video slides poster
  8. Abry, P., Fort, G., Pascal, B., & Pustelnik, N. (2022, September). Estimation et intervalles de crédibilité pour le taux de reproduction de la Covid19 par échantillonnage Monte Carlo Langevin proximal. GRETSI’22 XXVIIIème Colloque Francophone De Traitement Du Signal Et Des Images.
    paper
  9. Pascal, B., Mauduit, V., Abry, P., & Pustelnik, N. (2021, January). Scale-free Texture Segmentation: Expert Feature-based versus Deep Learning strategies. EUSIPCO 2020.
    paper video slides
  10. Busser, T., Pascal, B., Pustelnik, N., Abry, P., Serres, M., Philippe, R., & Vidal, V. (2019). Ecoulement gaz-liquide dans un milieu poreux confiné: caractérisation par analyse d’images. Rencontres Du Non-Linéaire 2019.
    paper
  11. Pascal, B., Busser, T., Pustelnik, N., Abry, P., & Vidal, V. (2019). Segmentation d’images texturées en grande dimension. Application à l’analyse d’écoulements multiphasiques. GRETSI 2019 XXVIIème Colloque Francophone De Traitement Du Signal Et Des Images.
    paper slides
  12. Pascal, B., Pustelnik, N., Abry, P., Serres, M., & Vidal, V. (2018). Joint estimation of local variance and local regularity for texture segmentation. Application to multiphase flow characterization. Proc. Int. Conf. Image Process., 2092–2096.
    paper poster
  13. Pascal, B., Pustelnik, N., Abry, P., & Pesquet, J.-C. (2018). Block-coordinate proximal algorithms for scale-free texture segmentation. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1253–1257.
    paper slides

Thesis

  1. Pascal, B. (2020). Estimation régularisée d’attributs fractals par minimisation convexe pour la segmentation de textures [École Normale Supérieure de Lyon].
    paper slides