Section: Scientific Foundations
Biological and Computer Vision
Another scientific focus of the team is the combined study of computer and biological vision. We think that a more detailed knowledge of the visual perception in humans and non-human primates can have a potential impact on algorithm design and performance evaluation. Thus, we develop so-called "bio-inspired" approaches to model visual tasks. This work is multidisciplinary. It involves knowledge from neuroscience and physiology, it tries to reproduce the percept and what psychophysics reveals from our visual system, and aims to compete with recent computer vision approaches.
The models that we develop are "bio-inspired" with regards to several aspects, depending on the scale chosen for the modelization.
At the microscopic level, see 3.1 above, we use spikes as a way to emit and code the information, which is certainly one explanation of the extraordinary performance of the visual system. For example, we have developped a spiking retinal simulator, called Virtual Retina, see 5.2 , which reproduces the main architecture and functions of retina layers, including contrast gain control  , [Oops!] . Also, our claim is that spikes represent a new efficient paradigm for computer vision applications. For example we developped a spiking model of V1 and MT layers, in order to categorize motion [Oops!] .
At the macroscopic level, we imitate the functional hierarchy of the visual cortex and propose the variational framework and integro-differential equations as a way to model cortical layers activity. For example, considering cortical maps modeled by a variational formulation, we show at a discrete level how to define a interactions between cortical columns [Oops!] . More generally, we show how to extend this formalism to model several coupled cortical maps, distinguishing forward and backward links.
We also develop phenomenological models, in order to reproduce a percept. For example, in [Oops!] , a model for motion estimation is proposed, integrating motion with form information, and we show how this model can handle various kind of stimuli classically used in psychophysics.
Validation of these models is crucial. Since we claim that our models are "bio-inspired", our goal is also to validate them through biology. For example, the spiking retina simulator (Virtual Retina) reproduces closely cell measurements done on cat ganglion cells, for various kinds of experiments. At the perceptual level, our models should also be able to reproduce a percept, which may be not trivial to reproduce with standard computer vision approaches. Computer vision is another way to prove the efficiency of our approaches, and it is one goal to show compare the performances of our approaches with respect to state-of-the-art computer vision approaches. This is currently done for example for action recognition, based on classical image databases.
This modeling activity brings new insight and tools for computer vision. But it also raises fundamental issues that will be the focus of future research. Understanding the neural code is certainly the most challenging one. Since we believe that spikes are one possible explanation of the visual system performance, and represent a new paradigm for computer vision, more fundamental work has to be done to understand how to better exploit the richness of this code.