Evaluating prosthetic vision by decoding phosphene images
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2022-07-27
Language
en
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Abstract
Globally, over 30 million people su↵er from
blindness. Visual prosthetics can help such people,
by stimulating some part of their visual
pathway, resulting in the perception of so-called
‘phosphenes’: small points in the visual field.
However, the fidelity and resolution of such stimulations
are currently limited. Thus, methods
to make the greatest use of this limited bandwidth
are necessary. To this end, a number of
phosphene encoders have been developed. Additionally,
in order to computationally evaluate
these phosphene encodings, methods that attempt
to extract useful information (such as the
original image) from the phosphene image are
necessary. In this thesis, I developed three such
phosphene decoders as well as a di↵erentiable
Canny edge detection encoder. I performed three
experiments: one to compare the informativity
of Canny edge detection encoding with object
contour encoding, one to evaluate the e↵ect of
phosphene resolution on informativity, and one
to evaluate the e↵ect of Canny edge detection
parameters on informativity, as well as the effectiveness
of di↵erentiable Canny edge detection.
I found that Canny edge detection yield
better performance in decoding the original image,
whereas object contour encoding yields better
performance in terms of optical flow and
semantic embedding decoding. Additionally, I
found that phosphene resolution strongly and
positively a↵ects decoding performance, except
for semantic embedding. Finally, I found that
clear patterns in the e↵ects of Canny edge detection
parameters on decoding performance could
be observed and that di↵erentiable Canny edge
detection easily overfits the training data.
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Faculteit der Sociale Wetenschappen