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Graph-Based Object Classification for Neuromorphic Vision Sensing

Bi, Y; Chadha, A; Abbas, A; Bourtsoulatze, E; Andreopoulos, Y; (2020) Graph-Based Object Classification for Neuromorphic Vision Sensing. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). (pp. pp. 491-501). IEEE: Seoul, Korea (South), Korea (South). Green open access

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Abstract

Neuromorphic vision sensing (NVS) devices represent visual information as sequences of asynchronous discrete events (a.k.a., "spikes'") in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, object classification with NVS streams cannot leverage on state-of-the-art convolutional neural networks (CNNs), since NVS does not produce frame representations. To circumvent this mismatch between sensing and processing with CNNs, we propose a compact graph representation for NVS. We couple this with novel residual graph CNN architectures and show that, when trained on spatio-temporal NVS data for object classification, such residual graph CNNs preserve the spatial and temporal coherence of spike events, while requiring less computation and memory. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we present and make available a 100k dataset of NVS recordings of the American sign language letters, acquired with an iniLabs DAVIS240c device under real-world conditions.

Type: Proceedings paper
Title: Graph-Based Object Classification for Neuromorphic Vision Sensing
Event: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICCV.2019.00058
Publisher version: https://doi.org/10.1109/ICCV.2019.00058
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10089483
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