Literature DB >> 33569551

Towards Making Videos Accessible for Low Vision Screen Magnifier Users.

Ali Selman Aydin1, Shirin Feiz1, Vikas Ashok2, I V Ramakrishnan1.   

Abstract

People with low vision who use screen magnifiers to interact with computing devices find it very challenging to interact with dynamically changing digital content such as videos, since they do not have the luxury of time to manually move, i.e., pan the magnifier lens to different regions of interest (ROIs) or zoom into these ROIs before the content changes across frames. In this paper, we present SViM, a first of its kind screen-magnifier interface for such users that leverages advances in computer vision, particularly video saliency models, to identify salient ROIs in videos. SViM's interface allows users to zoom in/out of any point of interest, switch between ROIs via mouse clicks and provides assistive panning with the added flexibility that lets the user explore other regions of the video besides the ROIs identified by SViM. Subjective and objective evaluation of a user study with 13 low vision screen magnifier users revealed that overall the participants had a better user experience with SViM over extant screen magnifiers, indicative of the former's promise and potential for making videos accessible to low vision screen magnifier users.

Entities:  

Keywords:  accessible videos; low vision; screen magnifiers; video magnifiers

Year:  2020        PMID: 33569551      PMCID: PMC7871698          DOI: 10.1145/3377325.3377494

Source DB:  PubMed          Journal:  IUI


  9 in total

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Journal:  IEEE Trans Vis Comput Graph       Date:  2017-09-11       Impact factor: 4.579

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Journal:  Vision Res       Date:  2005-08       Impact factor: 1.886

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Authors:  Syed Masum Billah; Vikas Ashok; Donald E Porter; I V Ramakrishnan
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2018-04

7.  Saliency in VR: How Do People Explore Virtual Environments?

Authors:  Vincent Sitzmann; Ana Serrano; Amy Pavel; Maneesh Agrawala; Diego Gutierrez; Belen Masia; Gordon Wetzstein
Journal:  IEEE Trans Vis Comput Graph       Date:  2018-04       Impact factor: 4.579

8.  Revisiting Video Saliency Prediction in the Deep Learning Era.

Authors:  Wenguan Wang; Jianbing Shen; Jianwen Xie; Ming-Ming Cheng; Haibin Ling; Ali Borji
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2020-12-04       Impact factor: 6.226

9.  Preliminary Evaluation of Two Digital Image Processing Strategies for Head-Mounted Magnification for Low Vision Patients.

Authors:  Ashley D Deemer; Bonnielin K Swenor; Kyoko Fujiwara; James T Deremeik; Nicole C Ross; Danielle M Natale; Chris K Bradley; Frank S Werblin; Robert W Massof
Journal:  Transl Vis Sci Technol       Date:  2019-02-28       Impact factor: 3.283

  9 in total
  4 in total

1.  Non-Visual Accessibility Assessment of Videos.

Authors:  Ali Selman Aydin; Yu-Jung Ko; Utku Uckun; I V Ramakrishnan; Vikas Ashok
Journal:  Proc ACM Int Conf Inf Knowl Manag       Date:  2021-10-30

2.  A Saliency-driven Video Magnifier for People with Low Vision.

Authors:  Ali Selman Aydin; Shirin Feiz; Vikas Ashok; I V Ramakrishnan
Journal:  Proc 17th Int Web All Conf (2020)       Date:  2020-04-20

3.  An implementation of Bubble Magnification did not improve the video comprehension of individuals with central vision loss.

Authors:  Francisco M Costela; Stephanie M Reeves; Russell L Woods
Journal:  Ophthalmic Physiol Opt       Date:  2021-03-28       Impact factor: 3.992

Review 4.  A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.

Authors:  Tharindu Kaluarachchi; Andrew Reis; Suranga Nanayakkara
Journal:  Sensors (Basel)       Date:  2021-04-03       Impact factor: 3.576

  4 in total

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