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Quantitative measures of crowding susceptibility in peripheral vision for large datasets

Author(s)
Shumikhin, Michael(Michael Andreevitch)
Thumbnail
Download1227511824-MIT.pdf (17.81Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Ruth Rosenholtz.
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. http://dspace.mit.edu/handle/1721.1/7582
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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).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/129227
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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  • Electrical Engineering and Computer Sciences - Master's degree
  • Electrical Engineering and Computer Sciences - Master's degree

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