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Authors: George Killick ; Gerardo Aragon-Camarasa and J. Paul Siebert

Affiliation: School of Computing Science, University of Glasgow, Glasgow, U.K.

Keyword(s): Foveated, Convolution, Retina, Implict, Neural, Representations.

Abstract: Foveated vision captures a visual scene at space-variant resolution. This makes the application of parameterized convolutions to foveated images difficult as they do not have a dense-grid representation in cartesian space. Log-polar space is frequently used to create a dense grid representation of foveated images, however this image representation may not be appropriate for all applications. In this paper we rephrase the convolution operation as the Monte-Carlo estimation of the filter response of the foveated image and a continuous filter kernel, an idea that has seen frequent use for deep learning on point clouds. We subsume our convolution operation into a simple CNN architecture that processes foveated images in cartesian space. We evaluate our system in the context of image classification and show that our approach significantly outperforms an equivalent CNN processing a foveated image in log-polar space.

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Paper citation in several formats:
Killick, G.; Aragon-Camarasa, G. and Siebert, J. (2022). Monte-Carlo Convolutions on Foveated Images. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 444-451. DOI: 10.5220/0010832400003124

@conference{visapp22,
author={George Killick. and Gerardo Aragon{-}Camarasa. and J. Paul Siebert.},
title={Monte-Carlo Convolutions on Foveated Images},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={444-451},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010832400003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Monte-Carlo Convolutions on Foveated Images
SN - 978-989-758-555-5
IS - 2184-4321
AU - Killick, G.
AU - Aragon-Camarasa, G.
AU - Siebert, J.
PY - 2022
SP - 444
EP - 451
DO - 10.5220/0010832400003124
PB - SciTePress

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