Wang Jing, Li Heng, Fu Weizhen, Chen Yao, Li Liming, Lyu Qing, Han Tingting, Chai Xinyu
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
College of Information Technology, Shanghai Ocean University, Shanghai, China.
Artif Organs. 2016 Jan;40(1):94-100. doi: 10.1111/aor.12498. Epub 2015 May 15.
Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients.
视网膜假体有恢复部分视力的潜力。在日常生活场景中进行目标识别是假体佩戴者的一项基本任务。由于仍受视网膜假体提供的低分辨率视觉感知的限制，研究和应用图像处理方法以向佩戴者传递更多有用的视觉信息非常重要。我们基于伊蒂（Itti）的视觉显著性图、感兴趣区域（ROI）提取和图像分割提出了两种图像处理策略。伊蒂的显著性模型从原始图像生成一个显著性图，其中显著区域通过模糊c均值聚类被分组为感兴趣区域。然后，Grabcut从标记的感兴趣区域图像生成一个原始对象，该对象与背景重新组合并通过两种方式增强——8-4分离像素化（8-4 SP）和背景边缘提取（BEE）。结果表明，与直接像素化（DP）相比，8-4 SP和BEE的识别准确率都显著更高。每种基于显著性的图像处理策略都取决于图像分割的性能。在良好和完美分割条件下，BEE和8-4 SP获得的识别准确率明显高于DP，而在分割条件较差时，只有BEE提高了性能。基于显著性的图像处理策略的应用被证实有利于在模拟假体视觉下的日常场景中的目标识别。它们有望帮助未来视网膜假体图像处理模块的开发，从而为患者提供更多益处。
