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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: Apr 14, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Simulated Prosthetic Vision: The Benefits of Computer-Based Object Recognition and Localization.

Marc J-M Macé¹, Valérian Guivarch¹, Grégoire Denis¹

¹CNRS & University of Toulouse, IRIT, Toulouse, France.

Artificial Organs

|April 23, 2015

Summary

This summary is machine-generated.

Advanced image processing can significantly improve vision restoration for blind patients using visual neuroprostheses. This study shows object localization enables effective visually guided reaching, even with low electrode counts.

Keywords:

Blindness Computer vision.Simulated prosthetic vision Visual impairment Visual neuroprosthesis

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Area of Science:

Biomedical Engineering
Neuroscience
Computer Vision

Background:

Current visual neuroprostheses offer limited vision, making simple tasks challenging for blind patients.
Simulated prosthetic vision (SPV) is crucial for evaluating future neuroprosthesis capabilities.
Previous SPV studies did not utilize advanced image processing techniques.

Purpose of the Study:

To investigate the potential of advanced image processing, specifically object localization, to enhance visual neuroprosthesis functionality.
To evaluate the usability of a simulated prosthetic vision device for object recognition, localization, and reaching tasks.
To determine the effectiveness of high-level information extraction for restoring visuomotor behaviors.

Main Methods:

Simulated a prosthetic vision device employing object localization in visual scenes.
Assessed the device's performance in object recognition, localization, and visually guided reaching.
Compared performance metrics (accuracy, speed) using different electrode counts (9 vs. 100).

Main Results:

A low number of electrodes (e.g., nine) proved sufficient for restoring visually guided reaching with reasonable timing and high accuracy.
Performance in both accuracy and speed was comparable between simulations using 9 and 100 electrodes.
High-level information extraction (object recognition and localization) significantly enhanced simulated prosthetic vision usability.

Conclusions:

Advanced image processing, particularly object localization, can drastically improve the functionality of current low-resolution visual neuroprostheses.
This object localization method shows promise for restoring various visuomotor behaviors in patients with visual impairments.
The reliability of vision algorithms remains a key limitation, though rapid advancements are expected.