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Related Concept Videos

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Related Experiment Video

Updated: Nov 17, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

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Towards Making Videos Accessible for Low Vision Screen Magnifier Users.

Ali Selman Aydin1, Shirin Feiz1, Vikas Ashok2

  • 1Stony Brook University.

IUI. International Conference on Intelligent User Interfaces
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces SViM, a novel screen magnifier interface that uses computer vision to identify important video regions for people with low vision. SViM improves the video viewing experience for low vision screen magnifier users.

Keywords:
accessible videoslow visionscreen magnifiersvideo magnifiers

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Area of Science:

  • Human-Computer Interaction
  • Computer Vision
  • Assistive Technology

Background:

  • Users with low vision face challenges with dynamic video content when using screen magnifiers.
  • Manually panning and zooming screen magnifiers is difficult with rapidly changing video frames.

Purpose of the Study:

  • To present SViM, a screen-magnifier interface designed to enhance video accessibility for low vision users.
  • To leverage video saliency models for automatic identification of regions of interest (ROIs) in videos.

Main Methods:

  • Developed SViM, a screen-magnifier interface integrating computer vision and video saliency models.
  • Implemented features for zooming, switching between ROIs, and assistive panning.
  • Conducted a user study with 13 low vision screen magnifier users.

Main Results:

  • SViM automatically identifies salient regions of interest (ROIs) in videos.
  • Users can interact with ROIs through zooming, clicking, and assistive panning.
  • The study demonstrated a better user experience with SViM compared to existing screen magnifiers.

Conclusions:

  • SViM shows promise in making video content more accessible for low vision screen magnifier users.
  • The integration of computer vision significantly improves interaction with dynamic digital content.