My research is in the areas of computer vision and visual neuroscience.
I believe that latest advances in neuroscience will bring novel concepts and ideas to computer science.
In particular, computer vision should benefit from such a multidisciplinary endeavor.
My goal is to conceive novel vision systems to solve computer vision problems, based on visual system properties.
To learn more, one slide about my research trajectory
(05/2016: new paper)
The wave of first spikes: a retinal information coding strategy revealed by large-scale multielectrode array recordings,
by G. Portelli, J.M Barrett, G. Hilgen, T. Masquelier, A. Maccione, S. Di Marco, L. Berdondini, P. Kornprobst, E. Sernagor,
(04/2016: new preprint) Pan-retinal characterization of Light Responses from Ganglion Cells in the Developing Mouse Retina,
by G. Hilgen, S. Pirmoradian, D. Pamplona, P. Kornprobst, B. Cessac, M. H Hennig, E. Sernagor.
Available on BioRxiv
(04/2016: new paper) Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision,
by N.V.K. Medathati, H. Neumann, G.S. Masson and P. Kornprobst,
Computer Vision and Image Understanding, 2016
(04/2016: new paper) Microsaccades enable efficient synchrony-based coding in the retina: a simulation study,
by T. Masquelier, G. Portelli and P. Kornprobst.
Scientific Reports 6, Article number: 24086, 2016
(04/2016: new conference) A new nonconvex variational approach for sensory neurons receptive field estimation,
by A. Drogoul, G. Aubert, B. Cessac and P. Kornprobst,
6th International Workshop on New Computational Methods for Inverse Problems (NCMIP).
(01/2016) Biovision team created!
Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor.
Frederic Cazals and Pierre Kornprobst, Eds, Springer, 2013:
Computational biology and biomedicine is a vast field where intensive research
is currently being carried out, with outstanding perspectives both in terms of
the complexity of the scientific problems to be addressed and technological
developments to be made. Taking up these challenges requires developing an
enhanced synergy between biology and biomedicine on the one hand, and applied
mathematics and computer science on the other hand. In line with this observation,
the motivation to write this book has been to show that researchers trained in
more quantitative and exact sciences can make major contributions in this
emerging discipline, and those with roots in biology and biomedicine can
benefit from a true leveraging power tailored to their specific needs.
Mathematical problems in image processing: Partial Differential Equations and the Calculus of Variations
G. Aubert and P. Kornprobst, Springer, Applied Mathematical Sciences, Vol 147, 2006 (second edition):
Amongst the numerous approaches which have been suggested, we focus on
Partial Differential Equations (PDE's), and Variational Approaches in this book.
Traditionally applied in physics, these methods have been successfully and
widely transferred in Computer Vision other the last decades. One of the main
interests in using PDEs is that the theory behind the concept is well-established.
Of course, PDEs are written in a continuous setting refering to analog images,
and once the existence and the uniqueness have been proven, we need to discretize
them in order to find a numerical solution. It is our conviction that reasoning
within a continuous framework makes the understanding of physical realities easier
and stimulates the intuition necessary to propose new models. We hope that this book
will illustrate this idea effectively.
Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision,
N.V.K. Medathati, H. Neumann, G.S. Masson and P. Kornprobst,
Computer Vision and Image Understanding (to appear).
Microsaccades enable efficient synchrony-based coding in the retina: a simulation study,
T. Masquelier, G. Portelli and P. Kornprobst.
Scientific Reports 6, Article number: 24086, 2016.
What can we expect from a V1-MT feedforward architecture for optical flow estimation?
F. Solari, M. Chessa, N.V.K. Medathati, and P. Kornprobst.
Note: Our code is available on ModelDB.
Bifurcation Study of a Neural Fields Competition Model with an Application to Perceptual Switching in Motion Integration,
J. Rankin, A. I. Meso, G. S. Masson, O. Faugeras, and P. Kornprobst.
Journal of Computational Neuroscience, Vol. 36, No. 2, pp. 193--213, 2014.
Bifurcation analysis applied to a model of motion integration with a multistable stimulus,
J. Rankin, È. Tlapale, R. Veltz, O. Faugeras, and P. Kornprobst.
Journal of Computational Neuroscience, Vol. 34, No. 1, pp. 103--124, 2013.
Try the stimulus (fix the center)!
Streaming an image through the eye: The retina seen as a dithered scalable image coder,
K. Masmoudi, M. Antonini, and P. Kornprobst.
Signal Processing: Image Communication 28 (8), pp. 856--869, 2013.
Frames for Exact Inversion of the Rank Order Coder,
K. Masmoudi and M. Antonini, and P. Kornprobst.
IEEE Transactions on Neural Networks and Learning Systems, 23(2):353-359, 2012.
Action recognition via bio–inspired features: The richness of center-surround interaction,
M.J. Escobar and P. Kornprobst.
Computer Vision and Image Understanding, 116(5):593-605, 2012.
You can watch the Audioslide presentation of Audioslide presentation of that paper
Variational multi-valued velocity field estimation for transparent sequences,
A. Ramirez, M. Rivera, Pierre Kornprobst, and F. Lauze.
Journal of Mathematical Imaging and Vision, 40(3):285–304, 2011.
Neural mechanisms of motion detection, integration, and segregation: From biology to artificial image processing systems.
J.D. Bouecke, E. Tlapale, P. Kornprobst, and H. Neumann.
EURASIP Journal on Advances in Signal Processing, vol 2011, Special issue on Biologically inspired signal processing: Analysis, algorithms, and applications, 2011.
Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism.
E. Tlapale, G. S. Masson, and P. Kornprobst.
Vision Research, 50(17):1676-1692, 2010.
Virtual Retina: A biological retina model and simulator, with contrast gain control.
A. Wohrer and P. Kornprobst.
Journal of Computational Neuroscience, 26(2):219, 2009.
This simulator is open-source (see Software section)
Bilateral Filtering: Theory and Applications.
S. Paris, P. Kornprobst, J. Tumblin, and F. Durand.
Foundations and Trends in Computer Graphics and Vision, 4(1):1-73, 2009.
This paper is a summary of tutorials given at SIGGRAPH and CVPR (see Teaching, talks and tutorial section)
Action Recognition Using a Bio-Inspired Feedforward Spiking Network.
M.-J. Escobar, G. S. Masson, T. Vieville, and P. Kornprobst.
International Journal of Computer Vision, 82(3):284, 2009.
Can the Nonlocal Characterization of Sobolev Spaces by Bourgain et al. Be Useful for Solving Variational Problems?
G. Aubert and P. Kornprobst.
SIAM J. Numer. Anal. 47(2):844-860, 2009.
How do high-level specifications of the brain relate to variational approaches?
T. Vieville, S. Chemla, and P. Kornprobst.
Journal of Physiology Paris, 101(1-3):118-135, 2007.
The use of superresolution techniques to reduce slice thickness in functional MRI.
R.R. Peeters, P. Kornprobst, M. Nikolova, S. Sunaert, T. Vieville, G. Malandain, R. Deriche, O. Faugeras, M. Ng, and P. Van Hecke.
International Journal of Imaging Systems and Technology (IJIST),
Special issue on High Resolution Image Reconstruction, 14:131–138, 2004.
Image sequence analysis via partial differential equations.
P. Kornprobst, R. Deriche, and G. Aubert.
Journal of Mathematical Imaging and Vision, 11(1):5-26, 1999.
A mathematical study of the relaxed optical flow problem in the space BV.
G. Aubert and P. Kornprobst.
SIAM Journal on Mathematical Analysis, 30(6):1282-1308, 1999.
Computing optical flow via variational techniques.
G. Aubert, R. Deriche, and P. Kornprobst.
SIAM Journal of Applied Mathematics, 60(1):156-182, 1999.
To learn more, you can check my full publication list,
including conferences with proceedings
Code and Software
A free access user-end software for the simulation of neural networks and the analysis of spike trains statistics intended to be used by the neuroscience community.
A paper describing it is in preparation.
AB filter: this library implements the physiological plausible filters proposed by Adelson and Bergen (1985)
Virtual Retina: A bio-inspired retina simulator for large-scale
spiking simulations. It transforms your videos into spike trains. You can install it or use our web-service.
A CImg plugin with classical image restoration and segmentation approaches (a new version is in preparation)
Teaching, talks and tutorials
Founder and coordinator of the Master of Science in Computational Biology and Biomedicine,
Université Nice Sophia Antipolis, from Nov. 2008 until Sept. 2011.
A Gentle Introduction to Bilateral Filtering and its Applications:
A class at ACM SIGGRAPH 2008, CVPR 2008 and ACM SIGGRAPH 2007 with
S. Paris, J. Tumblin, and F. Durand
Introduction to PDEs and variational approaches in image processing
Traitement des images numériques,
G. Aubert and P. Kornprobst. In J. Akoka and I. Comyn-Wattiau, editors,
Encyclopédie de l’informatique et des systèmes d’information, number 6, chapter 18, pages 861—879. Vuibert, November 2006.
Introduction to image processing (in french)
Join us on the Biological and Computer Vision Interfaces group
Follow me on ResearchGate
My Google Scholar profile
(Université Nice Sophia Antipolis, France),
(Universidad Técnica Federico Santa María, Chile),
(University of Edimburg, UK),
Guillaume S. Masson
(Insitut des Neurosciences de la Timone, France),
(Ulm University, Germany),
(New York University, USA),
(University of Newcastle, UK),
(Universite de Genes TODO, Italy),
(IIT, Genova, Italy),
(Institut de la Vision, France)
(Last update: August 2015)