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Leveraging Computer Vision Techniques for Video and Web Accessibility.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Leveraging Computer Vision Techniques for Video and Web Accessibility./
作者:	Aydin, Ali Selman.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	87 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28497821
ISBN:	9798516079870

Leveraging Computer Vision Techniques for Video and Web Accessibility.
Aydin, Ali Selman.

Leveraging Computer Vision Techniques for Video and Web Accessibility. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 87 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2021.

This item is not available from ProQuest Dissertations & Theses.

Interacting with multimedia content poses a major challenge for people with visual impairments. Making such multimedia content accessible to people with vision impairments has become all the more important because of the ever-increasing prevalence of such content, particularly videos, on the Internet. Recent advances in computer vision offer a promising avenue for tackling challenges in multimedia accessibility. In this thesis, we propose solutions to these accessibility problems by leveraging computer vision techniques. First, we introduce SViM, a saliency-driven video magnifier interface. SViM magnifies salient regions in a video, and offers auto-panning functionality, paving the way towards a better video watching experience for people with low vision. SViM provides three different magnifier interfaces along with accessibility customizations to further enhance the overall user experience. Quantitative and qualitative evaluation of SViM through user studies with low-vision participants demonstrate SViM's effectiveness compared to state-of-the-art alternatives. Second, we present SaIL, a method to automatically inject ARIA (Accessible Rich Internet Applications) landmarks in a webpage. Although ARIA landmarks have the potential to enhance web accessibility for people with vision impairments, they have remained under-utilized by web developers. SaIL allows screen reader users with visual impairments to rapidly skim and skip to important regions in a webpage. Third, we report our work on assessing the accessibility of videos for people who are blind. Towards this, we collected a dataset comprising subjective evaluations of video accessibility. We extract handcrafted features from both video and audio that show correlation with video accessibility, and utilize these features for predicting user generated video accessibility evaluations. Furthermore, these features play a significant role in diagnosis, namely identifying the causes of video inaccessibility.

ISBN: 9798516079870Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Multimedia content

Leveraging Computer Vision Techniques for Video and Web Accessibility.
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