Aydin Ali Selman, Feiz Shirin, Ashok Vikas, Ramakrishnan I V
Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
Department of Computer Science, Old Dominion University, Norfolk, VA, USA.
Proc 17th Int Web All Conf (2020). 2020 Apr;2020. doi: 10.1145/3371300.3383356. Epub 2020 Apr 20.
Consuming video content poses significant challenges for many screen magnifier users, which is the "go to" assistive technology for people with low vision. While screen magnifier software could be used to achieve a zoom factor that would make the content of the video visible to low-vision users, it is oftentimes a major challenge for these users to navigate through videos. Towards making videos more accessible for low-vision users, we have developed the SViM video magnifier system [6]. Specifically, SViM consists of three different magnifier interfaces with easy-to-use means of interactions. All three interfaces are driven by visual saliency as a guided signal, which provides a quantification of interestingness at the pixel-level. Saliency information, which is provided as a heatmap is then processed to obtain distinct regions of interest. These regions of interests are tracked over time and displayed using an easy-to-use interface. We present a description of our overall design and interfaces.
对于许多使用屏幕放大器的用户来说,观看视频内容带来了重大挑战,而屏幕放大器是视力低下人群常用的辅助技术。虽然可以使用屏幕放大器软件来实现一定的缩放比例,使视频内容对低视力用户可见,但对这些用户来说,在视频中进行导航通常是一项重大挑战。为了让低视力用户更方便地观看视频,我们开发了SViM视频放大器系统[6]。具体来说,SViM由三个不同的具有易于使用的交互方式的放大器界面组成。所有这三个界面都由视觉显著性作为引导信号驱动,视觉显著性在像素级别提供了兴趣度的量化。以热图形式提供的显著性信息随后被处理以获得不同的感兴趣区域。这些感兴趣区域会随着时间进行跟踪,并通过一个易于使用的界面进行显示。我们对我们的整体设计和界面进行了描述。