Indoor scenes understanding for visual prosthesis with fully convolutional networks
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Many computational models of prosthetic vision assume a linear relationship between stimulus parameters (e.g. amplitude) and phosphene appearance (e.g. brightness).
“Many computational models of prosthetic vision (Chen et al 2009, Perez-Yus et al 2017, Sanchez-Garcia et al 2019, Granley and Beyeler 2021) assume a linear relationship between stimulus parameters (e.g. amplitude) and phosphene appearance (e.g. brightness).”
From Axonal stimulation affects the linear summation of single-point perception in three Argus II users (2024) · cites this paper as Sanchez-Garcia et al 2019
Machine learning approaches such as convolutional neural networks and deep learning could recognise multiple objects in complex scenes, helping navigation, obstacle avoidance and target object finding.
“Machine learning approaches, such as convolutional neural networks [189] and deep learning [190,191], could recognise multiple objects in complex scenes, helping navigation, obstacle avoidance and target object finding.”
From Advances in visual prostheses: engineering and biological challenges (2022) · cites this paper as [189]
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References 14 · Cited by 16
- Authors
- Sanchez-Garcia, M., Martinez-Cantin, R., & Guerrero, J.
- Affiliations
- Universidad de Zaragoza (ES)
- I3A, Universidad de Zaragoza, Spain, Centro Universitario de la Defensa, Zaragoza and Spain, --- Select a Country ---
- Venue
- Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
- Published
- 2019
- Pages
- 218-225
- OpenAlex
- W2946780012
- Cited by
- 16
- References
- 14
- Citations by year
- 2025: 3 · 2024: 3 · 2022: 6 · 2021: 3 · 2020: 1
- FWCI
- 0.81
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (publishedVersion)
- International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- visual prosthesis, indoor scene understanding, fully convolutional network, blind rehabilitation
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