Authors:
Johannes Steffen
;
Jonathan Napp
;
Stefan Pollmann
and
Klaus Tönnies
Affiliation:
Otto-von-Guericke University, Germany
Keyword(s):
Bionic Vision, Retinal Implants, Artificial Neural Networks, Object Classification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Bioinformatics and Systems Biology
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Sensors and Early Vision
;
Signal Processing
;
Software Engineering
Abstract:
The restored vision by using subretinal implants of patients suffering from a loss of photoreceptors, e.g., in
retinitis pigmentosa and age-related macular degeneration, is, compared to healthy subjects, very limited.
Therefore, we investigated, whether it is possible to enhance the perception of such patients by transforming
the input images in a systematic manner. To this end, we propose a new image transformation network that is
capable to learn plausible image transformations in an end-to-end fashion in order to enhance the perception
of (virtual) patients with simulated subretinal implants. As a proof of concept, we test our method on an
object classification task with three classes. Our results are promising. Compared to a baseline model, the
overall object classification accuracy increased significantly from 67:4% to 81:1%. Furthermore, we discuss
implications and limitations of our proof of concept and outline aspects of our work that can be improved and
need to be subject of
further research.
(More)