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 primates can have a potential impact on algorithm design and performance. 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 what psychophysical experiments reveal and, as a final goal, we want to compete with recent computer vision approaches (see, e.g. [1] for a presentation of variational approaches in computer vision)
The models that we develop are bio-inspired with regards to several aspects, depending on the scale chosen for the modelization.
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At the microscopic level, one interesting aspect it to study the neural code: The nervous system use spikes as a way to emit and code the information, which is certainly one explanation of the extraordinary performance of the visual system. So we need to define a mathematical framework to be able to analyze this spiking langage and, based on those results, one can imagine some computer vision applications where spikes are used to code signals, [9] .
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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.
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We also develop phenomenological models, in order to reproduce a percept. For example, we are developing bio-inspired models for motion estimation, focusing on V1-MT and V2 layers and interactions, see [4] .
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.
