Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain.
PLoS One. 2020 Jan 29;15(1):e0227677. doi: 10.1371/journal.pone.0227677. eCollection 2020.
Prosthetic vision is being applied to partially recover the retinal stimulation of visually impaired people. However, the phosphenic images produced by the implants have very limited information bandwidth due to the poor resolution and lack of color or contrast. The ability of object recognition and scene understanding in real environments is severely restricted for prosthetic users. Computer vision can play a key role to overcome the limitations and to optimize the visual information in the prosthetic vision, improving the amount of information that is presented. We present a new approach to build a schematic representation of indoor environments for simulated phosphene images. The proposed method combines a variety of convolutional neural networks for extracting and conveying relevant information about the scene such as structural informative edges of the environment and silhouettes of segmented objects. Experiments were conducted with normal sighted subjects with a Simulated Prosthetic Vision system. The results show good accuracy for object recognition and room identification tasks for indoor scenes using the proposed approach, compared to other image processing methods.
假体视觉正在被应用于部分恢复视力受损者的视网膜刺激。然而，由于植入物的分辨率较差且缺乏颜色或对比度，因此产生的光幻视图像的信息带宽非常有限。对于假体使用者来说，他们在真实环境中识别物体和理解场景的能力受到严重限制。计算机视觉可以在克服这些限制和优化假体视觉中的视觉信息方面发挥关键作用，从而增加呈现的信息量。我们提出了一种新方法来为模拟光幻视图像构建室内环境的示意图表示。该方法结合了各种卷积神经网络，用于提取和传达有关场景的信息，例如环境的结构信息边缘和分割对象的轮廓。我们使用模拟假体视觉系统对正常视力的受试者进行了实验。结果表明，与其他图像处理方法相比，该方法在使用模拟假体视觉系统进行室内场景的物体识别和房间识别任务时具有较高的准确性。
