About Me
My overall research interest is the study of vision, from computational and biological perspectives.
Following the completion of my Masters and PhD in Applied Mathematics, my research was focused in the area of computer vision. In particular, my work was centered around PDEs and variational approaches as applied to vision (see the book Mathematical Problems in Image Processing, Springer, 2006, 2nd edition, co-authored with Gilles Aubert). More generally, I worked on image restoration and enhancement, inpainting super-resolution, optical flow estimation, interest-point detectors or salient event detection in videos.
Since 2002, I have mainly focused on biological vision, which involves modeling different aspects of the visual system taking into account advances from neuroscience and psychophysics. The aim is to understand and predict perceptual phenomena and to propose novel methods for computer vision problems. I worked on retina modeling and applications to new coding/decoding strategies, action recognition via bio-inspired models of V1/MT cortical areas, and neural fields models of motion integration.
I have published more than 40 papers in peer-reviewed journals and international conferences, and I supervised several PhD students and postdocs.
Now, I am convinced that latest advances in neuroscience will bring novel concepts and ideas to computer science. In particular, it is clear that computer vision should benefit from such a multidisciplinary endeavor. So, my goal is to conceive novel vision systems to solve computer vision problems, either based on the retina properties using recent advances in the MEA or based on the cortical hierarchy using the neural fields formalisms.
