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

  • Authors:

  • Ying Zhao;Yanyu Lu;Yukun Tian;Liming Li;Qiushi Ren;Xinyu Chai

  • Affiliations:

  • Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240 Shanghai, China;Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240 Shanghai, China;Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240 Shanghai, China;Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240 Shanghai, China;Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China;Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240 Shanghai, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

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 (8x8, 16x16, 24x24, 32x32, 48x48 and 64x64). The results showed that the mean recognition accuracy increased with the number of pixels. The recognition threshold for objects was within the interval of 16x16 to 24x24 pixels. For simple scenes, it was between 32x32 and 48x48 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.