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Object recognition based on a foreground extraction method under simulated prosthetic vision

机译：假肢视觉下基于前景提取方法的目标识别

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摘要

At present, retinal prostheses only generate low-resolution visual percepts because of a limited number of implantable electrodes. Prosthetic recipients are able to perform some simple visual tasks, but more complex tasks like object recognition are difficult. Therefore, image processing strategies to optimize the visual percepts of recipients were investigated. This study focused on object recognition under simulated prosthetic vision. A foreground extraction method based on a saliency model was proposed to obtain foreground object. Based on this, an image enhancement strategy combining edge information with foreground object was presented to obtain the pixelized image. Results showed that foreground extraction method achieved superior effects in foreground extraction. Psychophysical experiments verified that under simulated prosthetic vision, our method had prominent advantages in comparison with direct pixelization in terms of recognition accuracy and efficiency.
机译：目前，由于植入电极的数量有限，视网膜假体仅产生低分辨率的视觉感知。假肢接受者能够执行一些简单的视觉任务，但是像对象识别这样的更复杂的任务却很难。因此，研究了优化接收者视觉感知的图像处理策略。这项研究的重点是模拟假肢视觉下的物体识别。提出了一种基于显着性模型的前景提取方法来获取前景对象。在此基础上，提出了一种将边缘信息与前景物体相结合的图像增强策略，以获得像素化图像。结果表明，前景提取方法在前景提取中取得了较好的效果。心理物理实验证明，在模拟假肢视觉下，与直接像素化相比，我们的方法在识别准确性和效率上具有显着优势。

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