外围设备
周边视觉
相似性(几何)
人工智能
计算机科学
计算机视觉
医学
模式识别(心理学)
控制(管理)
资源(消歧)
验光服务
视觉系统
鉴定(生物学)
视觉感受
特征(语言学)
物理医学与康复
目视检查
视觉处理
视觉控制
视力障碍
相似
窗口(计算)
神经科学
作者
Claudio Maciel de Souza Coutinho,Maria C.O. Faria,Alexandre Bruckert,Suiyi Ling,Matthieu Perreira Da Silva,Ronaldo F. Zampolo,Patrick Le Callet
标识
DOI:10.1016/j.image.2026.117623
摘要
Visual field loss, caused by conditions like glaucoma or macular degeneration, affects many people and impacts several life domains. This study contributes to the exploration of how people with visual field loss, such as central and peripheral scotomas, process visual stimuli in digital environments. Current visual attention models are based on experimental data obtained from individuals with normal vision, often overlooking those with limited vision. To address this issue, we compare gaze data from subjects viewing stimuli under two conditions: with and without visual-field masks of varying radii, with the main goal of understanding the role played by different components of vision in the overall grasping of visual information. We use metrics commonly employed for benchmarking saliency models as a means of assessing the similarity between data from each obstruction mask and the control stimuli, which could lead to the conclusion of whether foveal and peripheral vision contribute equally to natural vision or whether one of them stands out in information extraction. Novel saliency models could use this information to predict attention from visually-impaired individuals by possibly balancing these two sources of vision. Our results show a significantly higher similarity between control and central-scotoma saliency maps than between control and peripheral-scotoma data. Another statistical analysis shows no substantial learning effect or familiarity bias when participants revisit the same image under different conditions in the eye-tracking experiment. Finally, a difference-significance study reveals that different radii from central-scotoma conditions demonstrated no meaningful dispersion from each other.
科研通智能强力驱动
Strongly Powered by AbleSci AI