Neural Coding Strategies for Event-Based Vision Data

Shane Harrigan; Sonya Coleman; Dermot Kerr; Y Pratheepan; Zheng Fang; Chengdong  Wu

doi:10.1109/ICASSP40776.2020.9054584

Neural Coding Strategies for Event-Based Vision Data

Shane Harrigan, Sonya Coleman, Dermot Kerr, Y Pratheepan, Zheng Fang, Chengdong Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

214 Downloads (Pure)

Abstract

Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.

Original language	English
Title of host publication	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Pages	2468-2472
Number of pages	5
ISBN (Electronic)	978-1-5090-6631-5
DOIs	https://doi.org/10.1109/ICASSP40776.2020.9054584
Publication status	Published (in print/issue) - 4 May 2020
Event	2020 IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain Duration: 4 May 2020 → 8 May 2020 https://2020.ieeeicassp.org

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2020-May
ISSN (Print)	1520-6149

Conference

Conference	2020 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated title	ICASSP
Country/Territory	Spain
City	Barcelona
Period	4/05/20 → 8/05/20
Internet address	https://2020.ieeeicassp.org

Bibliographical note

Publisher Copyright:
© 2020 IEEE

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

event-based vision
Convolutional Neural Network (CNN)
encoding scheme
Feature Extraction
Object recognition
Encoding scheme
Convolutional neural network
Feature extraction
Event-based vision

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICASSP40776.2020.9054584

IEEE_ICASSP2020Author Accepted Manuscript, 166 KB

Cite this

Harrigan, S., Coleman, S., Kerr, D., Pratheepan, Y., Fang, Z., & Wu, C. (2020). Neural Coding Strategies for Event-Based Vision Data. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 2468-2472). Article 9054584 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). https://doi.org/10.1109/ICASSP40776.2020.9054584

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title = "Neural Coding Strategies for Event-Based Vision Data",

abstract = "Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Presented are phase-of-firing and two sparse neural coding schemes. It is determined that machine learning approaches, i.e. Convolutional Neural Network combined with a Stacked Autoencoder network, produce powerful descriptors of the patterns within events. These coding schemes are deployed in an existing action recognition template and evaluated using two popular event-based data sets.",

keywords = "event-based vision, Convolutional Neural Network (CNN), encoding scheme, Feature Extraction, Object recognition, Encoding scheme, Convolutional neural network, Feature extraction, Event-based vision",

author = "Shane Harrigan and Sonya Coleman and Dermot Kerr and Y Pratheepan and Zheng Fang and Chengdong Wu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ; Conference date: 04-05-2020 Through 08-05-2020",

year = "2020",

month = may,

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Harrigan, S , Coleman, S , Kerr, D , Pratheepan, Y, Fang, Z & Wu, C 2020, Neural Coding Strategies for Event-Based Vision Data. in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings., 9054584, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, pp. 2468-2472, 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 4/05/20. https://doi.org/10.1109/ICASSP40776.2020.9054584

Neural Coding Strategies for Event-Based Vision Data. / Harrigan, Shane ; Coleman, Sonya ; Kerr, Dermot et al.
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. 2020. p. 2468-2472 9054584 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - Convolutional Neural Network (CNN)

KW - encoding scheme

KW - Feature Extraction

KW - Object recognition

KW - Encoding scheme

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KW - Feature extraction

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Harrigan S , Coleman S , Kerr D , Pratheepan Y, Fang Z, Wu C. Neural Coding Strategies for Event-Based Vision Data. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. 2020. p. 2468-2472. 9054584. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP40776.2020.9054584

Neural Coding Strategies for Event-Based Vision Data

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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