![]() Experiments with blind participants demonstrate the feasibility of our approach, which reaches accuracies over 90% for some participants. We adopt transfer learning with a deep learning system for user-defined multi-label k-instance classification. We explore personal object recognizers, where visually impaired people train a mobile application with a few snap shots of objects of interest and provide custom labels. Automatic object recog nition continues to provide limited assistance in such tasks because models tend to be trained on images taken by sighted people with different background clutter, scale, viewpoints, occlusion, and image quality than in photos taken by blind users. We derive guidelines for presenting visual output for low vision people and discuss opportunities for accessibility applications on this platform.īlind people often need to identify objects around them, from packages of food to items of clothing. Our study yielded preliminary evidence that mainstream AR glasses can be powerful accessibility tools. Identifying virtual elements had a similar effect on low vision and sighted people's walking speed, slowing it down slightly. ![]() We found that low vision participants were able to identify basic shapes and read short phrases on the glasses while sitting and walking. We also evaluated their ability to see the virtual elements while walking. ![]() We conducted a study with 20 low vision participants and 18 sighted controls, asking them to identify virtual shapes and text in different sizes, colors, and thicknesses. We sought to determine what low vision people could see on mainstream commercial augmented reality (AR) glasses, despite their visual limitations and the device's constraints. Unlike blind people, low vision people have functional vision and can potentially benefit from smart glasses that provide dynamic, always-available visual information. ![]() People with low vision have a visual impairment that affects their ability to perform daily activities. ![]()
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