Image processing based recognition of images with a limited number of pixels using simulated prosthetic vision

Visual prostheses based on micro-electronic technologies and biomedical engineering have been demonstrated to restore vision to blind individuals. It is necessary to determine the minimum requirements to achieve useful artificial vision for image recognition. To find the primary factors in common object and scene images recognition and optimize the recognition accuracy on low resolution images using image processing strategies, we investigate the effects of two kinds of image processing methods, two common shapes of pixels (square and circular) and six resolutions (8 × 8, 16 × 16, 24 × 24, 32 × 32, 48 × 48 and 64 × 64). The results showed that the mean recognition accuracy increased with the number of pixels. The recognition threshold for objects was within the interval of 16 × 16 to 24 × 24 pixels. For simple scenes, it was between 32 × 32 and 48 × 48 pixels. Near the threshold of recognition, different image modes had great impact on recognition accuracy. The images with “threshold pixel number and binarization-circular points” produced the best recognition results.