Quantitative measures of crowding susceptibility in peripheral vision for large datasets
Name
1227511824-MIT.pdf
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17.81 MB
Format
Adobe PDF
Checksum (MD5)
a147881e4bfdedffbc4ad7b441ff946a
Author(s)
Shumikhin, Michael(Michael Andreevitch)
Advisor(s)
Ruth Rosenholtz.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Peripheral vision is simulated using several trained generative neural networks. These networks map an image to a synthesized mongrel. A mongrel is an image simulating the visual phenomenon of crowding that a normal human would experience in the periphery. Mongrels of natural scenes and font types are explored in this thesis. These synthesized mongrels and base images were scored by feature similarity to determine an images' quantitative susceptibility to the crowding phenomenon. The quantitative measure is used to determine the most and least susceptible fonts to crowding in a large data set of fonts.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 101-103).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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