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Pierre GUILBERT

LYON

En résumé

I am a computer vision engineer at Kitware. I work on autonomous vehicles problems in computer vision and deep-learning with a particular interest in non-linear optimization.

Mes compétences :
C++
Deep-Learning
Computer Vision
Mathématiques
Numerical Optimization

Entreprises

  • Kitware - Computer Vision Engineer

    2016 - maintenant Developing exciting open sources softwares and algorithms in Computer Vision (multi-view geometry, 3D reconstruction, Bundle Adjustement, SLAM, Deep learning and related fields)

    ⇨ I designed and implemented a State-Of-the-Art Lidar-based SLAM algorithm:
    https://blog.kitware.com/veloview-lidar-slam-capabilities/

    One of the main developer of VeloView, an open source software for Lidar visualization
    https://github.com/Kitware/VeloView
    https://www.paraview.org/Wiki/VeloView

    https://blog.kitware.com/detect-locate-and-seize-with-veloview-actemium-and-kitware-demonstrate-the-industrial-capabilities-of-pattern-recognition-in-lidar-point-clouds/

  • I-PRI services - Computer Vision Research Engineer

    2015 - 2016 Traitement d'image appliqué au domaine de la santé. Traitement et analyse d'images du fond de l’œil issus de de bases de données internationales.

  • Cnrs - Research Assistant

    Paris 2015 - 2015 The Logarithmic Image Processing (LIP) model provides a physical and mathematical framework for image processing. The aim of this master thesis project was to develop this model in two directions :
    - Improvement of image and stabilization of the variation of illumination
    - Creation of metrics based on the LIP model

  • NT2I - Computer Vision Engineer

    2014 - 2014 In the context of on-vehicle cameras, the objective was to develop motion measurement algorithms in video. The project also focused on the choice of the embedded system running these algorithms

Formations

  • Université Lyon 1 Claude Bernard

    Villeurbanne 2014 - 2015 Master of Science (M.Sc.), Mathematics and Computer Science

    ♦Numerical analysis: Non linear problem solving, Numerical optimization, Inverse problem solving, Variational methods, numerical partial differential equation solving

    ♦Machine Learning: (Recursive / Convolutional)-Neural Network, Autoencoder, Generative Adversial Network, Support Vector Machine, Graphical Models

    ♦Signal processing: Frequency analysis, wavelets, denoising, linear / nonlinear filt

  • CPE Lyon

    Villeurbanne 2012 - 2015 Diplôme d'ingénieur, Mathematics and Computer Science, Grade A (First 10%)

    ♦Numerical analysis: Non linear problem solving, Numerical optimization, Inverse problem solving, Variational methods, numerical partial differential equation solving

    ♦Machine Learning: (Recursive / Convolutional)-Neural Network, Autoencoder, Generative Adversial Network, Support Vector Machine, Graphical Models

    ♦Signal processing: Frequency analysis, wavelets, denoising, linear / nonlinear filt

  • Lycée Champollion

    Grenoble 2010 - 2012 Classes préparatoires aux grandes écoles MPSI - MP*, Grade A

    Pure mathematics: Universal algebra, (multi)-Linear algebra, Real and Complex Analysis, Measure theory, Group Theory, Topology, Differential Geometry

    Physic: Fluid mechanics, Solid mechanics, Continuum mechanics, Thermodynamics, Electromagnetism, Electronic

Réseau

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