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Towards synergistic models of motion information processing in biological and artificial vision

Abstract : In this thesis, we studied the problem of motion estimation in mammals and propose that scaling up models rooted in biology for real world applications can give us fresh insights into the biological vision. Using a classic model that describes the activity of directionally-selective neurons in V1 and MT areas of macaque brain, we proposed a feedforward V1-MT architecture for motion estimation and benchmarked it on computer vision datasets (first publicly available evaluation for this kind of models), revealing interesting shortcomings such as lack of selectivity at motion boundaries and lack of spatial association of the flow field. To address these, we proposed two extensions, a form modulated pooling strategy to minimize errors at texture boundaries and a regression based decoding scheme. These extensions improved estimation accuracy but also reemphasized the debate about the role of different cell types (characterized by their tuning curves) in encoding motion, for example relative role of pattern cells versus component cells. To understand this, we used a phenomenological neural fields model representative of a population of directionally tuned MT cells to check whether different tuning behaviors could be reproduced by a recurrently interacting population or if we need different types of cells explicitly. Our results indicated that a variety of tuning behavior can be reproduced by a minimal network, explaining dynamical changes in the tuning with change of stimuli leading us to question the high inhibition regimes typically considered by models in the literature.
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Submitted on : Monday, November 20, 2017 - 1:10:01 PM
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  • HAL Id : tel-01639367, version 2



Naga Venkata Kartheek Medathati. Towards synergistic models of motion information processing in biological and artificial vision. Computer Vision and Pattern Recognition [cs.CV]. Université Côte d'Azur, 2016. English. ⟨NNT : 2016AZUR4127⟩. ⟨tel-01639367v2⟩



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