An, L, Yan, Z, Wang, W et al. (2 more authors) (2022) Enhancing Visual Coding Through Collaborative Perception. IEEE Transactions on Cognitive and Developmental Systems. p. 1. ISSN 2379-8920
Abstract
A central challenge facing the nature human-computer interaction involves understanding how neural circuits process visual perceptual information to improve the user’s operation ability under complex tasks. Visual coding models aim to explore the biological characteristics of retinal ganglion cells to provide quantitative predictions of responses to a range of visual stimuli. The existing visual coding models lack adaptability in natural and complex scenes. Therefore this paper proposes an enhanced visual coding model through collaborative perception. Our model first extracts the multi-modal spatiotemporal features of the input video to simulate the retinal response characteristics adaptively. Secondly, it uses the basis function to compile the input stimulus into a multi-modal stimulus matrix. Afterward, the upstream and downstream filters reform the stimulus matrix to generate the spike sequence. Experiments show that the proposed model reproduces the physiological characteristics of ganglion cells in the biological retina, leading to the high accuracy, good adaptability, and biological interpretability in comparison with its rivals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Sep 2022 14:20 |
Last Modified: | 05 Sep 2022 14:20 |
Status: | Published online |
Publisher: | IEEE |
Identification Number: | 10.1109/tcds.2022.3203422 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190614 |