Ozimek, P., Hristozova, N., Balog, L. and Siebert, J. P. (2019) A space-variant visual pathway model for data efficient deep learning. Frontiers in Cellular Neuroscience, 13, 36. (doi: 10.3389/fncel.2019.00036)
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Abstract
We present an investigation into adopting a model of the retino-cortical mapping, found in biological visual systems, to improve the efficiency of image analysis using Deep Convolutional Neural Nets (DCNNs) in the context of robot vision and egocentric perception systems. This work has now enabled DCNNs to process input images approaching one million pixels in size, in real time, using only consumer grade graphics processor (GPU) hardware in a single pass of the DCNN.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Ozimek, Peter and Siebert, Dr Paul and Hristozova, Nina |
| Authors: | Ozimek, P., Hristozova, N., Balog, L., and Siebert, J. P. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | Frontiers in Cellular Neuroscience |
| Publisher: | Frontiers Media |
| ISSN: | 1662-5102 |
| ISSN (Online): | 1662-5102 |
| Copyright Holders: | Copyright © 2019 Ozimek, Hristozova, Balog and Siebert |
| First Published: | First published in Frontiers in Cellular Neuroscience 13: 36 |
| Publisher Policy: | Reproduced under a Creative Commons License |
| Data DOI: | 10.5525/gla.researchdata.744 |
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