Optimization of visual information presentation for visual prosthesis
Open accessAbstract
Visual prosthesis applying electrical stimulation to restore visual function for the blind has promising prospects. However, due to the low resolution, limited visual field, and the low dynamic range of the visual perception, huge loss of information occurred when presenting daily scenes. The ability of object recognition in real-life scenarios is severely restricted for prosthetic users. To overcome the limitations, optimizing the visual information in the simulated prosthetic vision has been the focus of research. This paper proposes two image processing strategies based on a salient object detection technique. The two processing strategies enable the prosthetic implants to focus on the object of interest and suppress the background clutter. Psychophysical experiments show that techniques such as foreground zooming with background clutter removal and foreground edge detection with background reduction have positive impacts on the task of object recognition in simulated prosthetic vision. By using edge detection and zooming technique, the two processing strategies significantly improve the recognition accuracy of objects. We can conclude that the visual prosthesis using our proposed strategy can assist the blind to improve their ability to recognize objects. The results will provide effective solutions for the further development of visual prosthesis.
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Other features included contrast information, semantic information, depth information, surface normal, convolutional filters, and the use of clip-art style images.
“Other features included contrast information,73,76 semantic information,80,82,83 depth information,76,79,81 surface normal,84 convolutional filters,73,75,77 and the use of clip-art style images.85,86”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [80]
Section 2.4 describes a strategy that enhances contrast between the main object and the background, supporting the contrast-information part of the sentence. The cited paper does not substantiate the listed uses of semantic information, depth information, surface normals, convolutional filters, or clip-art-style images. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDue to the complexity of saliency calculation, real-time performance is the limiting factor for visual prostheses.
“However, due to the complexity of saliency calculation, real-time performance is the limiting factor for visual prostheses [185][186][187][188].”
From Advances in visual prostheses: engineering and biological challenges (2022) · cites this paper as [187]
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Cites
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289 · see citing works
Global estimates of visual impairment: 2010
Pascolini, D., & Mariotti, S. P. (2011) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2011-300539 · Cited by 3,773 · see citing works
Global contrast based salient region detection
Cheng, M., Zhang, G., Mitra, N. J., Huang, X., & Hu, S. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995344 · Cited by 3,072 · see citing works
Saliency Detection via Graph-Based Manifold Ranking
Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.407 · Cited by 2,554 · see citing works
Context-Aware Saliency Detection
Goferman, S., Zelnik‐Manor, L., & Tal, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.272 · Cited by 1,636 · see citing works
Artificial vision with wirelessly powered subretinal electronic implant alpha-IMS
Stingl, K., Bartz-Schmidt, K. U., Besch, D., Braun, A., Bruckmann, A., Gekeler, F., Greppmaier, U., Hipp, S., Hörtdörfer, G., Kernstock, C., et al. (2013) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2013.0077 · Cited by 473 · see citing works
Automatic salient object segmentation based on context and shape prior
Jiang, H., Wang, J., Yuan, Z., et al. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.110 · Cited by 458 · see citing works
Veraart, C., Raftopoulos, C., Mortimer, J. T., Delbeke, J., Pins, D., Michaux, G., Vanlierde, A., Parrini, S., & Wanet-Defalque, M.-C. (1998) · Brain Research · doi:10.1016/s0006-8993(98)00977-9 · Cited by 407 · see citing works
Frequency-tuned salient region detection
Achanta, R., Hemami, S. S., Estrada, F., & Susstrunk, S. E. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206596 · Cited by 378 · see citing works
Pattern recognition with the optic nerve visual prosthesis
Veraart, C., Wanet‐Defalque, M., Gérard, B., Vanlierde, A., & Delbeke, J. (2003) · Artificial Organs · doi:10.1046/j.1525-1594.2003.07305.x · Cited by 259 · see citing works
Weiland, J. D., & Humayun, M. S. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2314733 · Cited by 186 · see citing works
Visually Guided Performance of Simple Tasks Using Simulated Prosthetic Vision
Hayes, J. S., Yin, V. T., Piyathaisere, D. V., Weiland, J. D., Humayun, M. S., & Dagnelie, G. (2003) · Artificial Organs · doi:10.1046/j.1525-1594.2003.07309.x · Cited by 162 · see citing works
Position, size and luminosity of phosphenes generated by direct optic nerve stimulation
Delbeke, J., Oozeer, M., & Veraart, C. (2003) · Vision Research · doi:10.1016/s0042-6989(03)00013-0 · Cited by 138 · see citing works
Beyeler, M., Rokem, A., Boynton, G. M., & Fine, I. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa795e · Cited by 102 · see citing works
Retinal prosthesis phosphene shape analysis
Nanduri, D., Humayun, M. S., Greenberg, R. J., McMahon, M. J., & Weiland, J. D. (2008) · 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2008.4649524 · Cited by 74 · see citing works
Saliency-based image processing for retinal prostheses
Parikh, N., Itti, L., & Weiland, J. D. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/1/016006 · Cited by 70 · see citing works
Li, H., Su, X., Wang, J., Kan, H., Han, T., Zeng, Y., & Chai, X. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.001 · Cited by 59 · see citing works
The effect of blur adaptation on accommodative response and pupil size during reading
Le, R., Bao, J., Chen, D., He, J., & Lü, F. (2010) · Journal of Vision · doi:10.1167/10.14.1 · Cited by 53 · see citing works
Active confocal imaging for visual prostheses
Jung, J., Aloni, D., Yitzhak Yitzhaky, B., & Eli Peli, A. (2014) · Vision Research · doi:10.1016/j.visres.2014.10.023 · Cited by 51 · see citing works
Zhao, Y., Lü, Y., Tian, Y., Li, L., Ren, Q., & Chai, X. (2010) · Information Sciences · doi:10.1016/j.ins.2010.04.021 · Cited by 47 · see citing works
Simulating prosthetic vision: Optimizing the information content of a limited visual display
van Rheede, J. J., Christopher Kennard, C., & Hicks, S. L. (2010) · Journal of Vision · doi:10.1167/10.14.32 · Cited by 46 · see citing works
Wang, J., Lü, Y., Gu, L., Zhou, C., & Chai, X. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.136 · Cited by 42 · see citing works
Region-of-interest processing for electronic visual prostheses
Boyle, J. R., Maeder, A. J., & Boles, W. W. (2008) · Journal of Electronic Imaging · doi:10.1117/1.2841708 · Cited by 36 · see citing works
Wang, J., Li, H., Fu, W., Yao, C., Li, L., Lyu, Q., Han, T., & Chai, X. (2015) · Artificial Organs · doi:10.1111/aor.12498 · Cited by 27 · see citing works
Li, H., Han, T., Wang, J., Lu, Z., Cao, X., Chen, Y., Li, L., Zhou, C., & Chai, X. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.014 · Cited by 27 · see citing works
Recognition of Objects in Simulated Irregular Phosphene Maps for an Epiretinal Prosthesis
Lü, Y., Wang, J., Wu, H., Li, L., Cao, X., & Chai, X. (2013) · Artificial Organs · doi:10.1111/aor.12174 · Cited by 16 · see citing works
Lü, Y., Kan, H., Liu, J., Wang, J., Chen, T., Yao, C., Ren, Q., Hu, J., & Chai, X. (2013) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.12-11039 · Cited by 15 · see citing works
[Research progress of visual prosthesis].
Yan, Y., Chai, X., Chen, Y., Zhou, C., Ren, Q., & Li, L. (2016) · PubMed · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [16]Harel J. Koch C. Perona P. Graph-based visual saliency Proceedings of the 20th Annual Conference on Neural Information Processing Systems (NIPS '06) December 2006 545 552 2-s2.0-84864037603
- [18]Zhou D. Weston J. Gretton A. Bousquet O. Scholkopf B. Ranking on data manifolds Advances In NIPS 2003 Ranking on data manifolds. Advances In NIPS B. Scholkopf 169 176
Cited by
Sorted by citations
Semantic and structural image segmentation for prosthetic vision
Sanchez-Garcia, M., Martinez-Cantin, R., & Guerrero, J. J. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227677 · Cited by 62 · see citing works
de Ruyter van Steveninck, J., van Gestel, T., Koenders, P., van der Ham, G., Vereecken, F., Güçlü, U., van Gerven, M., Güçlütürk, Y., & van Wezel, R. (2022) · Journal of Vision · doi:10.1167/jov.22.2.1 · Cited by 51 · see citing works
Advances in visual prostheses: engineering and biological challenges
Borda, E., & Ghezzi, D. (2022) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ac812c · Cited by 44 · see citing works
van der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rueckauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2024) · eLife · doi:10.7554/elife.85812 · Cited by 39 · see citing works
“…been proposed in the literature, ranging from basic edge detection, or contour-detection algorithms ( Boyle et al., 2001 ; Dowling et al., 2004 ; Guo et al., 2018 ) to more intelligent deep learning-based approaches, which can be tailored to meet task-specific demands ( Sanchez-Garcia et al.,…”
BackgroundOptimization of Neuroprosthetic Vision via End-to-end Deep Reinforcement Learning
Küçükoğlu, B., Rueckauer, B., Ahmad, N., de Ruyter van Steveninck, J., Güçlü, U., & van Gerven, M. (2022) · doi:10.1101/2022.02.25.482017 · Cited by 25 · see citing works
van der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rückauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2022) · doi:10.1101/2022.12.23.521749 · Cited by 39 · see citing works
PVGAN: a generative adversarial network for object simplification in prosthetic vision
Elnabawy, R. H., Abdennadher, S., Hellwich, O., & Eldawlatly, S. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac8acf · Cited by 3 · see citing works
Visual Prostheses in the Era of Artificial Intelligence Technology
Sarbout, I., Gungor, A., Ounissi, M., Zaher, S., Ptito, M., Kupers, R., Racoceanu, D., & Milea, D. (2025) · Eye and Brain · doi:10.2147/eb.s524322 · Cited by 3 · see citing works
“Other features included contrast information, 73,76 semantic information, 80,82,83 depth information, 76,79,81 surface normal, 84 convolutional filters, 73,75,77 and the use of clip-art style images.”
A computational model to design wide field-of-view optic nerve neuroprostheses
Micera, S., Romeni, S., De Luca, D., Pierantoni, L., Toni, L., Marino, G., & Moccia, S. (2023) · doi:10.21203/rs.3.rs-3218482/v1 · Cited by 2 · see citing works
- Authors
- Guo, F., Yang, Y., & Gao, Y.
- Affiliations
- Xi'an University of Technology (CN)
- Corresponding author
- Yang, Yuan
- Venue
- International Journal of Biomedical Imaging
- Publisher
- Wiley
- Published
- 2018
- Volume
- 2018
- Pages
- 1-12
- ISSN
- 1687-4188 (ISSN-L)
- 1687-4196
- PMC
- PMC5872628
- OpenAlex
- W2791016185
- Cited by
- 27
- References
- 28
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 5 · 2022: 6 · 2021: 3 · 2020: 2 · 2019: 2
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Biomedical Imaging (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Funds Youth Science Fund Project (61102017, 12JK0499)
- Education Department of Shaanxi Province (61102017, 12JK0499)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual prosthesis, electrical stimulation, visual information presentation, salient object detection, object recognition, blinds, edge detection, zooming, background clutter removal, simulated prosthetic vision
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