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Action Recognition Using a Bio-Inspired Feedforward Spiking Network

Maria-Jose Escobar 1 Guillaume S. Masson 2 Thierry Viéville 3 Pierre Kornprobst 1 
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
3 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (Proceedings of the 11th international conference on computer vision, pp. 1–8, 2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience.
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Contributor : Thierry Viéville Connect in order to contact the contributor
Submitted on : Monday, July 27, 2009 - 7:11:42 PM
Last modification on : Thursday, March 17, 2022 - 10:08:29 AM


  • HAL Id : inria-00407903, version 1


Maria-Jose Escobar, Guillaume S. Masson, Thierry Viéville, Pierre Kornprobst. Action Recognition Using a Bio-Inspired Feedforward Spiking Network. International Journal of Computer Vision, Springer Verlag, 2009, 82 (3), pp.284-301. ⟨inria-00407903⟩



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