Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36304
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Maier, Patrick
Rainey, James
Gheorghiu, Elena
Appiah, Kofi
Bhowmik, Deepayan
Contact Email: elena.gheorghiu@stir.ac.uk
Title: Digit Classification using Biologically Plausible Neuromorphic Vision
Issue Date: 2024
Date Deposited: 10-Aug-2024
Conference Name: Applications of Digital Image Processing XLVII
Abstract: Despite tremendous advancement in computer vision, especially with deep learning, understanding scenes in the wild remains challenging. Even modern image classification models often misclassify when presented with out-of-distribution inputs despite having been trained on tens of millions of images or more. Moreover, training modern deep-learning classifiers requires a lot of energy due to the need to iterate many times over the training set, constantly updating billions of model parameters. Owing to problems with generalisability and robustness as well as efficiency, there is growing interest in computer vision to mimic biological vision (e.g., human vision) in the hope that doing so will require fewer resources for training both in terms of energy and in terms of data sets while increasing robustness and generalisability. This paper proposes a biologically plausible neuromorphic vision system that is based on a spiking neural network and is evaluated on the classification of hand-written digits from the MNIST dataset. The experimental outcome indicates improved robustness of the proposed approach over state-of-the-art considering non-digit detection.
Status: AM - Accepted Manuscript
Rights: Publisher allows this work to be made available in this repository. Published in Proceedings of SPIE with the following policy: SPIE grants to authors (and their employers) of papers, posters, and presentation recordings published in Proceedings of SPIE the right to post an author-prepared version or the officially published version (preferred) on an internal or external repository controlled exclusively by the author/employer, or the entity funding the research, provided that (a) such posting is noncommercial and the publication is made available to users without charge; (b) an appropriate SPIE attribution and citation appear with the publication; and (c) a DOI link to SPIE’s official online version of the publication is provided. Please cite as: Patrick Maier, James Rainey, Elena Gheorghiu, Kofi Appiah, and Deepayan Bhowmik "Digit classification using biologically plausible neuromorphic vision", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131370I (30 September 2024); https://doi.org/10.1117/12.3031280

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