Using a Secondary Network for Foveation in Computer Vision
Keywords
Loading...
Authors
Issue Date
2020-01-31
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
Foveation plays a important role in the human visual system, but is currently hardly
used in computer vision. Even though the "input" and systems used in computer vision
are quite di erent from human vision, it is plausible foveation provides some bene ts.
In order to investigate this I compared classi cation losses of foveated images and
non-foveated images. In addition I trained a Deep-Q Network to learn which foveation
locations are optimal for a given image. Results show that there is a bene t of
foveation, and that it is possible for a computer vision model to learn which foveation
locations are optimal.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen