Phosphene Vision and AI: Representing the World
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2019-07-01
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en
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Abstract
Prosthetic vision relies on the observation that stimulating the visual cortex
generates small visual percepts called phosphenes. Due to biological limitations,
current cortical implants have low resolution. Researchers started investigating
more complex image processing techniques in an attempt to circumvent these lim-
itations and improve the quality of the phosphene image. Semantic segmentation
has been proposed as a potential solution to this issue. However, these complex
algorithms are computationally demanding. We investigated whether there are any
advantages in using complex image processing techniques such as semantic segmen-
tation over simpler methods, here represented by edge detection. We compared
the two methods through a visual search task in simulated phosphene vision on
healthy subjects, measuring reaction times as well as subjective opinions. Our re-
sults show that participants performed better under the edge detection condition.
The current state of the semantic segmentation model used in this work needs to
be improved before it can yield better results than edge detection.
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Faculteit der Sociale Wetenschappen