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Communication Dans Un Congrès Année : 2007

From bio-inspired vision toolbox to real-time computer vision algorithms

Résumé

In this paper, we make the link between biological knowledge of the Human Visual System (HVS) and computer vision processing. The goal is to show the advantage of using biological perception models in all day life computer vision applications. On the one hand, biological researches on the HVS allow the exploration of the physiological properties of Human to see, to understand, to learn and to adapt to the environment. The human retina and the primary visual cortex V1 have been one of the main explored parts of the HVS. This enthusiastic topic gives a better understanding of the different cells networks involved from a biological point of view and models their effect from a signal processing point of view. Some models have been proposed [1, 2, 3] and have already shown interesting human-like vision perception properties. The main properties are linked to the adaptivity of the HVS which allows to " see " with a good reliability in various conditions from sunlight to shadow. Real-time retina models allow imagining their use in today's vision applications needs and some of them already show the potential such as artificial retina [4], image enhancement [5]. On the other hand, computer vision is a wide research area in which several mathematical approaches are used in order to solve visual perception problems. Various research axis is explored such as objects recognition and tracking, face analysis, visual scene exploration, etc... All these computer vision tasks are in general achieved easily by humans. Nevertheless, they are solved with complex mathematical approaches which are not linked to human perception. The proposed algorithms are often sensitive to environment changes (luminance variations, acquisition noise etc.) and require complex setup stages in order to obtain good performances in specific conditions. We propose here to show how it is possible to use the efficient real-time retina model proposed in [4] and the V1 primary cortex model of [6] to achieve real-time computer vision processing. The spatio-temporal properties of the retina model allow a robust spatio-temporal contours extraction by modelling the parvocellular (details) and magnocellular (motion) channels which are robust against noise and luminance variations. These two channels are the basis of the computer vision algorithms that we propose. Coupled with the V1 cortex model which allows object and shape recognition, we propose here application examples: A face analysis system which detects the open or closed eyes and mouth states by using some energetic properties of the studied face features allowing hypo-vigilance detection for drivers; A moving object segmentation and tracking system based on the description of the motion energy generated by the moving objects; And finally an object classifier based on the object texture and structure analysis. All these applications are exclusively based on a bio-inspired architecture. They show the interest to involve the knowledge of the HSV in order to solve computer vision applications. Several other computer vision applications can exploit such a philosophy opening a new way to solve vision problems.
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Dates et versions

hal-00258617 , version 1 (22-02-2008)

Identifiants

  • HAL Id : hal-00258617 , version 1

Citer

Alexandre Benoit, Alice Caplier, Barthélémy Durette, Jeanny Hérault. From bio-inspired vision toolbox to real-time computer vision algorithms. European Retina Meeting, Oct 2007, Frankfurt/Main, Germany. pp.S4-P02. ⟨hal-00258617⟩
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