Kévin Polisano

Kévin Polisano

CNRS researcher in applied mathematics

Laboratoire Jean Kuntzmann

Biography

I am a CNRS researcher in the SVH team of Laboratoire Jean Kuntzmann (LJK) at University Grenoble Alpes (UGA). My research interests include image and signal processing, wavelets, convolutional neural networks, stochastic processes and graphs analysis.

I was previously a postdoctoral fellow in a LIG team for the Data Institute of UGA where I worked with Éric Gaussier, Adeline Leclercq-Samson, Julien Chevallier and Jean-Marc Francony. I have defended my PhD thesis in December 2017, under the supervision of Valérie Perrier, Marianne Clausel and Laurent Condat. Before that, I was graduated from the École nationale supérieure d’informatique et de mathématiques appliquées, an engineering school of the Grenoble INP Institute of Technology.

For more information please find a resumé or my curriculum vitæ (in french)

Interests
  • Image and signal processing
  • Machine learning
  • Stochastic processes
  • Graphs analysis
Education

  • PhD in applied mathematics, 2017

    University Grenoble Alpes

  • Engineering degree (MSc) from Ensimag, 2013

    Grenoble INP Institute of Technology

Publications

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H. Leterme, K. Polisano, V. Perrier, K. Alahari (2022). On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks.

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S. Achard, I. Gannaz, K. Polisano (2022). Génération de Modèles Graphiques. GRETSI 2022 - XXVIIIème Colloque Francophone de Traitement Du Signal et Des Images.

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H. Leterme, K. Polisano, V. Perrier, K. Alahari (2022). From CNNs to Shift-Invariant Twin Wavelet Models.

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H. Leterme, K. Polisano, V. Perrier, K. Alahari (2021). Modélisation Parcimonieuse de CNNs Avec Des Paquets d'Ondelettes Dual-Tree. ORASIS 2021 - Journées Francophones Des Jeunes Chercheurs En Vision Par Ordinateur.

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K. Polisano, M. Clausel, V. Perrier, L. Condat (2019). Riesz-Based Orientation of Localizable Gaussian Fields. Applied and Computational Harmonic Analysis.

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K. Polisano, L. Condat, M. Clausel, V. Perrier (2019). Une Approche Convexe de La Super-Résolution et de La Régularisation de Lignes 2D Dans Les Images. XXVIIème Colloque GRETSI (GRETSI 2019).

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K. Polisano, L. Condat, M. Clausel, V. Perrier (2019). A Convex Approach to Superresolution and Regularization of Lines in Images. SIAM Journal on Imaging Sciences.

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K. Polisano, M. Clausel, L. Condat, V. Perrier (2018). Simulation of Oriented Patterns with Prescribed Local Orientation Using Anisotropic Gaussian Fields.

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Kévin Polisano, Marianne Clausel, Valérie Perrier, Laurent Condat (2018). A Convex Approach to Super-Resolution and Regularization of Lines in Images.

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K. Polisano (2017). Modélisation de textures anisotropes par la transformée en ondelettes monogéniques.

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K. Polisano, L. Condat, M. Clausel, V. Perrier (2016). Convex Super-Resolution Detection of Lines in Images.

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K. Polisano, L. Condat, M. Clausel, V. Perrier (2016). Convex Super-Resolution Detection of Lines in Images. 2016 24th European Signal Processing Conference (EUSIPCO).

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K. Polisano, M. Clausel, V. Perrier, L. Condat (2015). Modélisations de Textures Par Champ Gaussien à Orientation Locale Prescrite. GRETSI 2015 - XXVème Colloque Francophone de Traitement Du Signal et Des Images.

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K. Polisano, M. Clausel, V. Perrier, L. Condat (2014). Texture Modeling by Gaussian Fields with Prescribed Local Orientation. 2014 IEEE International Conference on Image Processing (ICIP).

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Talks

DATA seminar 2023
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks
Nov 23, 2023 2:00 PM Grenoble
Hubert Leterme, Kévin Polisano, Karteek Alahari, Valérie Perrier
Slides
DATA seminar 2023
Workshop ASCETE 2023
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks
Nov 8, 2023 9:00 AM Grenoble
Hubert Leterme, Kévin Polisano, Karteek Alahari, Valérie Perrier
Slides
Workshop ASCETE 2023
Poster GRETSI 2022
Generation of graphical models
Sep 6, 2022 9:00 AM Nancy
Kévin Polisano, Sophie Achard, Irène Gannaz
PDF
Poster GRETSI 2022
DATA seminar
Riesz-based orientation of localizable Gaussian fields
Feb 25, 2021 2:00 PM Grenoble
Kévin Polisano
Slides Video
DATA seminar

Contact

Teaching

2020-2023

Wavelets and applications

  • Course
    • Lecture 1 – From Fourier to the 1D Continuous Wavelet Transform
    • Lecture 2 – Wavelet zoom: a local characterization of functions
    • Lecture 3 – The 2D Continuous Wavelet Transform
    • Lecture 4 – The 1D Discrete Wavelet Transform and 1D Multi-resolution Analysis
    • Lecture 5 – The 2D Discrete Wavelet Transform and 2D Multi-resolution Analysis
    • Lecture 6 – Linear and nonlinear approximations in wavelet bases
    • Lecture 7 – The scattering transform
    • Lecture 8 – The Laplacian of a graph and its applications
    • Lecture 9 – The graph Fourier transform and wavelets on graphs
  • Lab tutorials

Course content