Deep video-to-video transformations for accessibility applications

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Banda, Dalitso Hansini.

Advisor dc:contributor.advisor

• Boris Katz.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121622

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121622