Performance of visually guided tasks using simulated prosthetic vision and saliency-based cues
Open accessAbstract
OBJECTIVE: The objective of this paper is to evaluate the benefits provided by a saliency-based cueing algorithm to normally sighted volunteers performing mobility and search tasks using simulated prosthetic vision. APPROACH: Human subjects performed mobility and search tasks using simulated prosthetic vision. A saliency algorithm based on primate vision was used to detect regions of interest (ROI) in an image. Subjects were cued to look toward the directions of these ROI using visual cues superimposed on the simulated prosthetic vision. Mobility tasks required the subjects to navigate through a corridor, avoid obstacles and locate a target at the end of the course. Two search task experiments involved finding objects on a tabletop under different conditions. Subjects were required to perform tasks with and without any help from cues. RESULTS: Head movements, time to task completion and number of errors were all significantly reduced in search tasks when subjects used the cueing algorithm. For the mobility task, head movements and number of contacts with objects were significantly reduced when subjects used cues, whereas time was significantly reduced when no cues were used. The most significant benefit from cues appears to be in search tasks and when navigating unfamiliar environments. SIGNIFICANCE: The results from the study show that visually impaired people and retinal prosthesis implantees may benefit from computer vision algorithms that detect important objects in their environment, particularly when they are in a new environment.
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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 [73]
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“Simulations use phosphenes that are very regular in their appearance, whereas the appearance of phosphenes in visual prosthesis subjects can be irregular [33, 34].”
Humayun, M. S., Dorn, J. D., Ahuja, A. K., Caspi, A., Filley, E., Dagnelie, G., Salzmann, J., Santos, A., Duncan, J., daCruz, L., et al. (2009) · 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2009.5332695 · Cited by 100 · see citing works
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Advances in retinal prosthesis systems
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Cortical visual prostheses: From microstimulation to functional percept
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Semantic and structural image segmentation for prosthetic vision
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End-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2022) · Journal of Vision · doi:10.1167/jov.22.2.20 · Cited by 54 · see citing works
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Mobility and low contrast trip hazard avoidance using augmented depth
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“…studies have examined the effect of the number of phosphenes, spacing between phosphenes and the visual angle over which the phosphenes are spread (e.g. de Ruyter van Steveninck et al., 2022b ; Thorn et al., 2020 ; Sanchez-Garcia et al., 2022 ; Srivastava et al., 2009 ; Parikh et al., 2013 ).”
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Optimization 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
End-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2020) · doi:10.1101/2020.12.19.423601 · Cited by 54 · see citing works
“…to earlier work in the field of simulated prosthetic vision (Bollen, Guclu, et al., 2019; Bollen, van Wezel, et al., 2019; Dagnelie et al., 2007; Parikh et al., 2013; Sanchez-Garcia et al., 2020; Vergnieux et al., 2017; Vergnieux et al., 2014), the previous experiments in this study are…”
MethodResultvan 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
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
- Parikh, N., Itti, L., Humayun, M., & Weiland, J.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Parikh, N
- Itti, L
- Weiland, J
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2013-02-28
- Volume
- 10
- Issue
- 2
- Pages
- 026017
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2041713173
- Cited by
- 54
- References
- 6
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 7 · 2021: 5 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 5 · 2016: 4 · 2015: 5 · 2014: 5 · 2013: 1
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0827764, 0310723, W81XWH-10-2-0076)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- simulated prosthetic vision, visual search, obstacle avoidance, retinal prosthesis, visually impaired people, region of interest detection, primate vision, head movement, object localization
- MeSH terms
- Algorithms, Computer Simulation, Cues, Humans, Image Processing, Computer-Assisted, Psychomotor Performance, Visual Fields, Head Movements, Visual Prosthesis
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