June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Perceived visual field expanding with content-aware image retargeting for prosthetic vision
Author Affiliations & Notes
  • Xinyu Chai
    Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Yajie Zeng
    Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Heng Li
    Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Yao Chen
    Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Chuanqing Zhou
    Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  • Footnotes
    Commercial Relationships   Xinyu Chai, None; Yajie Zeng, None; Heng Li, None; Yao Chen, None; Chuanqing Zhou, None
  • Footnotes
    Support  he National Natural Science Foundation of China (61273368, 61472247); The National Basic Research Program of China (973 Program, 2011CB7075003); National High Technology Research and Development Program of China (863 Program, 2009AA04Z326).
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 4206. doi:
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    • Get Citation

      Xinyu Chai, Yajie Zeng, Heng Li, Yao Chen, Chuanqing Zhou; Perceived visual field expanding with content-aware image retargeting for prosthetic vision. Invest. Ophthalmol. Vis. Sci. 2017;58(8):4206.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Although great progress has been made in the development of retinal prostheses, small visual field and low-resolution remain as major obstacles to achieve satisfying prosthetic vision. Hence, we focus on expanding the small visual field by image retargeting methods, aiming to retarget a wider scene into the prosthetic vision and meanwhile preserve important information as much as possible.

Methods : We utilized the idea of the content-aware seam-assisted shrinkability (SAS) (Al-Atabany et al, 2010) method, and made improvements by weighting a color-based saliency map to enhanced the image importance.
We compared our method with cropping, scaling and SAS under simulated prosthetic vision (SPV) by object detection and recognition experiments. 28 subjects with normal or corrected-to-normal vision participated in the study. 85 images (48 indoor and 37 outdoor) of 40° visual angle were compressed to 20° by four image strategies. After direct pixelization, there were four groups of images, cropping pixelization (C-P), scaling pixelization (S-P), SAS pixelization (SAS-P) and OURS-P.

Results : On the method improvement, our method outperforms in preserving more important region of an image when resizing than SAS under different compression ratios based on MSRA100 database.
About the experimental results, for indoor images, the percentage of detected objects were 45.47% ± 1.31% for C-P, 92.71% ± 1.11% for S-P, 97.48% ± 0.64% for SAS-P and 98.78% ± 0.42% for OURS-P. The latter three strategies had a significant difference from C-P. SAS-P and OURS-P both had a significant difference from S-P. The recognition accuracy were 23.61% ± 1.26% for C-P, 35.30% ± 3.47% for S-P. SAS-P (54.08% ± 2.63%) and OURS-P (64.11% ± 2.28%) showed a significant increase (P < 0.001) than C-P and S-P. The average recognition time for C-P was 7.69 ± 0.83 s, 10.62 ± 1.22 s for S-P, 9.52 ± 0.53 s for SAS-P, and 9.94 ± 1.21 s for OURS-P. And there was similar results for outdoor images.

Conclusions : We demonstrated the color-based saliency map contributed to preserve more important information. The experimental results indicated our method outperformed in conveying more useful visual information and meanwhile guaranteeing the recognition performance under low-resolution SPV. Therefore, this image processing strategy may be beneficial to prosthesis recipients in the future.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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