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Object recognition from sparse simulated phosphenes and curved segments
Elsa Scialom1, Udo A Ernst2, David Rotermund2
1Laboratory of Psychophysics, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015 Lausanne, Switzerland.
Vision Research
|November 7, 2025
Summary
Researchers determined the minimum number of visual elements needed for object recognition using simulated phosphenes. Using contour segments, rather than round patches, significantly reduced the required elements for visual prostheses.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Computer Vision
Background:
- Cortical prostheses aim to restore vision by stimulating neurons to create phosphenes.
- Current limitations in the number of simultaneously elicited phosphenes hinder object recognition for prosthetic vision.
Purpose of the Study:
- To determine the minimum number of discrete visual elements required for object recognition.
- To evaluate the impact of patch shape on the number of elements needed for recognition in simulated prosthetic vision.
Main Methods:
- Replaced object contours with varying numbers of round patches to simulate phosphenes.
- Conducted a free-naming task with 46 sighted participants to identify fragmented objects.
- Compared recognition performance using round patches versus straight and curved segments.
Main Results:
- Object recognition required between 29 and 65 round patches, depending on the object.
- Using contour segments reduced the number of elements needed for recognition by 27% compared to round patches.
- Findings suggest contour information offers perceptual benefits for visual prostheses.
Conclusions:
- This study provides a lower bound estimate for the number of phosphenes necessary for object recognition in visual prostheses.
- The results offer realistic design parameters for future simultaneous stimulation protocols in cortical visual prostheses.
- Leveraging contour information can potentially reduce the element count for improved object recognition.
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