Modélisation de la dynamique de l'intégration contextuelle du mouvement chez le primate
Émilien Tlapale · HAL (Le Centre pour la Communication Scientifique Directe) · 2011
This thesis addresses the study of motion integration in the primate. Based on anatomical and functional knowledge of two cortical areas involved in motion perception, namely v1 and mt, we explain various perceptual and oculo-motor responses found in the literature. First, we build a recurrent model of motion integration where a minimal number of cortical interactions are assumed. Pro- posing a simple readout mechanism, we are able to reproduce not only motion perception but also the dynamics of smooth pursuit eye movements on various line figures and gratings viewed through different apertures. Second, following perceptual studies concerning motion integration and physiological studies of re- ceptive fields, we construct another dynamical model where motion information is gated by form cues. To this end, we postulate that the visual cortex takes ad- vantage of luminance smoothness in order to gate motion diffusion. Such an ele- mentary diffusion mechanism allows to solve various contextual problems where extrinsic junctions should be eliminated, without relying on complex junction de- tectors or depth computation. Finally, we rewrite the initial dynamical model into the neural fields formalism in order to mathematically analyse its properties. We incorporate the multiplicative feedback term into the formalism, and prove the existence and uniqueness of the solution. To generalise the comparison against visual performance, we propose a new evaluation methodology based on human visual performance and design a database of image sequences taken from biology and psychophysics literature. Indeed, offering proper evaluation methodology is essential to continue progress in modelling the neural mechanisms involved in motion processing. To conclude, we investigate the performances of our neural fields model by comparison against state of the art computer vision approaches and sequences. We find that, despite its original objective, this model gives results comparable to recent computer vision approaches of motion estimation.