Deep video-to-video transformations for accessibility applications
Name
1098049248-MIT.pdf
Size
6 MB
Format
Adobe PDF
Checksum (MD5)
5d32ffd96999979e3712a9d15678bdda
Author(s)
Banda, Dalitso Hansini.
Advisor(s)
Boris Katz.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
We develop a class of visual assistive technologies that can learn visual transforms to improve accessibility as an alternative to traditional methods that mostly rely on extracted symbolic information. In this thesis, we mainly focus on how we can apply this class of systems to address photosensitivity. People with photosensitivity may have seizures, migraines or other adverse reactions to certain visual stimuli such as flashing images and alternating patterns. We develop deep learning models that learn to identify and transform video sequences containing such stimuli whilst preserving video quality and content. Using descriptions of the adverse visual stimuli, we train models to learn transforms to remove such stimuli. We show that these deep learning models are able to generalize to real-world examples of images with these problematic stimuli. From our experimental trials, human subjects rated video sequences transformed by our models as having significantly less problematic stimuli than their input. We extend these ideas; we show how these deep transformation networks can be applied in other visual assistive domains through demonstration of an application addressing the problem of emotion recognition in those with the Autism Spectrum Disorder.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 73-79).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Persistent DSpace Link
