McCarthy Chris, Walker Janine G, Lieby Paulette, Scott Adele, Barnes Nick
NICTA Computer Vision Research Group, Building A, 7 London Circuit, Canberra, Australia. Research School of Engineering, Australian National University, Canberra, Australia.
J Neural Eng. 2015 Feb;12(1):016003. doi: 10.1088/1741-2560/12/1/016003. Epub 2014 Nov 26.
We evaluated a novel visual representation for current and near-term prosthetic vision. Augmented depth emphasizes ground obstacles and floor-wall boundaries in a depth-based visual representation. This is achieved by artificially increasing contrast between obstacles and the ground surface via a novel ground plane extraction algorithm specifically designed to preserve low-contrast ground-surface boundaries.
The effectiveness of augmented depth was examined in human mobility trials compared against standard intensity-based (Intensity), depth-based (Depth) and random (Random) visual representations. Eight participants with normal vision used simulated prosthetic vision with 20 phosphenes and eight perceivable brightness levels to traverse a course with randomly placed small and low-contrast obstacles on the ground.
The number of collisions was significantly reduced using augmented depth, compared with intensity, depth and random representations (48%, 44% and 72% less collisions, respectively).
These results indicate that augmented depth may enable safe mobility in the presence of low-contrast obstacles with current and near-term implants. This is the first demonstration that an augmentation of the scene ensuring key objects are visible may provide better outcomes for prosthetic vision.
我们评估了一种用于当前和近期假肢视觉的新型视觉表示方法。增强深度在基于深度的视觉表示中突出了地面障碍物和地面与墙壁的边界。这是通过一种专门设计用于保留低对比度地面边界的新型地平面提取算法,人为增加障碍物与地面之间的对比度来实现的。
在人体移动性试验中,将增强深度与基于标准强度的(强度)、基于深度的(深度)和随机的(随机)视觉表示进行比较,检验增强深度的有效性。八名视力正常的参与者使用具有20个光幻视和八个可感知亮度级别的模拟假肢视觉,在地面上有随机放置的小的和低对比度障碍物的路线上穿行。
与强度、深度和随机表示相比,使用增强深度时碰撞次数显著减少(分别减少48%、44%和72%)。
这些结果表明,增强深度可能使当前和近期植入物在存在低对比度障碍物的情况下实现安全移动。这是首次证明增强场景以确保关键物体可见可能为假肢视觉带来更好的效果。