Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision
Abstract
Background and objective Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision.Method We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects.Results i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene.Conclusion The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant.
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Due 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 [185]
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Beyeler, M., & Sanchez-Garcia, M. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/aca69d · Cited by 34 · see citing works
“Based on this premise, researchers have developed various image optimization strategies, and assessed their performance by having sighted observers conduct daily visual tasks under SPV (Dagnelie et al 2007, Al-Atabany et al 2010, McCarthy et al 2014, Li et al 2018).”
Optimization of visual information presentation for visual prosthesis
Guo, F., Yang, Y., & Gao, Y. (2018) · International Journal of Biomedical Imaging · doi:10.1155/2018/3198342 · Cited by 27 · see citing works
Deep Learning--Based Scene Simplification for Bionic Vision
Han, N., Srivastava, S., Xu, A., Klein, D., & Beyeler, M. (2021) · arXiv · doi:10.48550/arxiv.2102.00297 · Cited by 4 · 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
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
- Li, H., Su, X., Wang, J., Kan, H., Han, T., Zeng, Y., & Chai, X.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Shanghai Ocean University (CN)
- Corresponding author
- Chai, Xinyu
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 84
- Pages
- 64-78
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2767602077
- Cited by
- 59
- References
- 100
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 6 · 2023: 8 · 2022: 13 · 2021: 8 · 2020: 4 · 2019: 5 · 2018: 2
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61273368, 61472247, 61773256)
- Ministry of Education of the People's Republic of China (B08020)
- National High-tech Research and Development Program (2009AA04Z326)
- National Key Research and Development Program of China (2011CB707503)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- retinal prosthesis, simulated prosthetic vision, object recognition, GrabCut, foreground segmentation, pixelation, phosphenes
- MeSH terms
- Facial Recognition, Adult, Artificial Intelligence, Eye Movements, Female, Humans, Image Processing, Computer-Assisted, Male, Markov Chains, Phosphenes, Prosthesis Design, Vision, Ocular, Visual Perception, Persons with Visual Disabilities, Visually Impaired Persons, Recognition, Psychology, Young Adult, Visual Prosthesis
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