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Liu et al., 2020 - Google Patents

Unsupervised aer object recognition based on multiscale spatio-temporal features and spiking neurons

Liu et al., 2020

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Document ID
7155708255774122336
Author
Liu Q
Pan G
Ruan H
Xing D
Xu Q
Tang H
Publication year
Publication venue
IEEE transactions on neural networks and learning systems

External Links

Snippet

This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio- temporal feature (MuST) representation of input AER events and a spiking neural network …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

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    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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