InforLorV4, Hal, Curation, bibRecord, 000A88

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

Identifieur interne : 000A88 ( Hal/Curation ); précédent : 000A87; suivant : 000A89

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

Auteurs : Maria-Jose Escobar [France] ; Guillaume Masson [France] ; Thierry Viéville [France] ; Pierre Kornprobst [France]

Source :

International Journal of Computer Vision [ 0920-5691 ] ; 2009.

RBID : Hal:inria-00407903

English descriptors

mix :
- Action recognition, Bio-inspired model, MT, Motion analysis, Spiking networks, V1.

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.

Url:

https://hal.inria.fr/inria-00407903

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Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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<abstract xml:lang="en">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.</abstract>
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Action Recognition Using a Bio-Inspired Feedforward Spiking Network

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

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