A connectionist approach for visual perception of motion
Identifieur interne : 006B54 ( Main/Merge ); précédent : 006B53; suivant : 006B55A connectionist approach for visual perception of motion
Auteurs : Claudio Castellanos S Chez ; Bernard Girau ; Frédéric AlexandreSource :
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Abstract
Modeling visual perception of motion by connectionist networks offers various areas of research for the development of real-time models of dynamic perception-action. In this paper we present the bases of a bio-inspired connectionist approach that is part of our development of neural networks applied to autonomous robotics. Our model of visual perception of motion is based on a causal adaptation of spatiotemporal Gabor lters. We use our causal spatiotemporal lters within a modular and strongly localized architecture that performs a shunting inhibition mechanism. This model has been evaluated on arti cial as well as natural image sequences.
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<author><name sortKey="Girau, Bernard" sort="Girau, Bernard" uniqKey="Girau B" first="Bernard" last="Girau">Bernard Girau</name>
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<author><name sortKey="Alexandre, Frederic" sort="Alexandre, Frederic" uniqKey="Alexandre F" first="Frédéric" last="Alexandre">Frédéric Alexandre</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>bio-inspired neural networks</term>
<term>gabor filters</term>
<term>optical flow</term>
<term>perception of motion</term>
<term>shunting inhibition</term>
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<front><div type="abstract" xml:lang="en" wicri:score="2251">Modeling visual perception of motion by connectionist networks offers various areas of research for the development of real-time models of dynamic perception-action. In this paper we present the bases of a bio-inspired connectionist approach that is part of our development of neural networks applied to autonomous robotics. Our model of visual perception of motion is based on a causal adaptation of spatiotemporal Gabor lters. We use our causal spatiotemporal lters within a modular and strongly localized architecture that performs a shunting inhibition mechanism. This model has been evaluated on arti cial as well as natural image sequences.</div>
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