School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
Artif Intell Med. 2018 Jan;84:64-78. doi: 10.1016/j.artmed.2017.11.001. Epub 2017 Nov 10.
Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision.
We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects.
i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene.
The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant.
目前的视网膜假体只能产生低分辨率的视觉感知，这些感知由电极阵列引起，具有不可控的颜色和有限的灰度。在这种视觉感知下，假体接受者只能完成一些简单的视觉任务，但更复杂的任务，如面部识别/物体识别，极其困难。因此，有必要研究和应用图像处理策略，以优化接受者的视觉感知。本研究专注于使用模拟假体视觉来识别感兴趣的物体。
我们使用了一种基于生物启发的基于图的视觉显著度模型的显著度分割方法和基于 grabCut 的自适应迭代优化框架，以自动提取前景对象。在此基础上，进一步利用两种图像处理策略，即单独像素化加法和背景像素收缩，来增强提取的前景对象。
i）通过心理物理实验验证的结果表明，在模拟假体视觉下，这两种策略在识别准确率和效率方面均明显优于直接像素化。ii）我们还发现，两种策略下的识别性能与分割结果相关，并且受到场景中配对相关物体的积极影响。
使用显著度分割方法和图像处理策略可以自动提取和增强前景对象，并显著提高植入高密度植入物的接受者的物体识别性能。
