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Authors: Johannes Steffen ; Georg Hille and Klaus Tönnies

Affiliation: Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg and Germany

Keyword(s): Bionic Vision, Retinal Implants, Vision Restauration.

Related Ontology Subjects/Areas/Topics: Applications ; Bioinformatics and Systems Biology ; Pattern Recognition ; Sensors and Early Vision ; Software Engineering

Abstract: This work addresses the automatic enhancement of visual percepts of virtual patients with retinal implants. Specifically, we render the task as an image transformation problem within an artificial neural network. The neurophysiological model of (Nanduri et al., 2012) was implemented as a tensor network to simulate a virtual patient’s visual percept and used together with an image transformation network in order to perform end-to-end learning on an image reconstruction and a classification task. The image reconstruction task was evaluated using the MNIST data set and yielded plausible results w.r.t. the learned transformations while halving the dissimilarity (mean-squared-error) of an input image to its simulated visual percept. Furthermore, the classification task was evaluated on the cifar-10 data set. Experiments show, that classification accuracy increases by approximately 12.9% when a suitable input image transformation is learned.

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Paper citation in several formats:
Steffen, J.; Hille, G. and Tönnies, K. (2019). Automatic Perception Enhancement for Simulated Retinal Implants. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 908-914. DOI: 10.5220/0007695409080914

@conference{icpram19,
author={Johannes Steffen. and Georg Hille. and Klaus Tönnies.},
title={Automatic Perception Enhancement for Simulated Retinal Implants},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={908-914},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007695409080914},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Automatic Perception Enhancement for Simulated Retinal Implants
SN - 978-989-758-351-3
IS - 2184-4313
AU - Steffen, J.
AU - Hille, G.
AU - Tönnies, K.
PY - 2019
SP - 908
EP - 914
DO - 10.5220/0007695409080914
PB - SciTePress

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