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Boris Katz. Banda, Dalitso Hansini. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-07-15T20:28:44Z 2019-07-15T20:28:44Z 2018 2018 https://hdl.handle.net/1721.1/121622 1098049248 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). 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. by Dalitso Hansini Banda. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 79 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Deep video-to-video transformations for accessibility applications Thesis application/pdf 2019-07-15T20:28:42Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3adaba7b-1307-4804-89de-44bd67055784"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Deep video-to-video transformations for accessibility applications</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Banda, Dalitso Hansini.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>