Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Encapsulation enhances stromal cell survival for safe neonatal therapy: a pilot study.

World journal of pediatric surgery·2026

Same author

Distribution, bioconcentration, trophic transfer and effects of combined exposure of ionic liquid [C<sub>8</sub>mim]Br and PMMA in a simulated ecosystem.

Journal of environmental sciences (China)·2026

Same author

Thread- reinforced encapsulation devices (THRED) for therapeutic stem cell delivery.

Journal of pediatric surgery·2026

Same author

Symptom experience subtypes and their association with social isolation in older adults with diabetes and coronary heart disease: a latent profile analysis study.

Frontiers in medicine·2026

Same author

Mesenchymal stromal cell therapy restores intestinal integrity and attentuates inflammation in a preterm piglet model of necrotizing enterocolitis.

Pediatric surgery international·2026

Same author

Selective electrosynthesis of urea from nitrate and carbon dioxide with low overpotential.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

The application of computer vision to visual prosthesis.

Jing Wang^1,2, Haiyi Zhu¹, Jianyun Liu¹

¹School of Information, Shanghai Ocean University, Shanghai, China.

Artificial Organs

|July 28, 2021

Summary

Visual prostheses create artificial sight by converting images into electrical signals. This review explores using computer vision to enhance low-resolution prosthetic vision for better visual perception in patients.

Keywords:

computer vision image processing machine learning simulated prosthetic vision visual prosthesis

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Related Experiment Videos

Last Updated: Jun 23, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Area of Science:

Biomedical Engineering
Computer Vision
Neuroscience

Background:

Visual prostheses aim to restore vision for patients with incurable blinding diseases.
Current devices face limitations due to low electrode counts, resulting in suboptimal visual perception.
Improving visual function requires optimizing information processing for low-resolution prosthetic vision.

Purpose of the Study:

To review recent advancements in prosthetic vision research utilizing computer vision methods.
To analyze the application of computer vision in simulating and optimizing prosthetic visual percepts.
To discuss strategies for enhancing visual perception and device efficiency in visual prostheses.

Main Methods:

Review of existing literature on computer vision techniques applied to visual prostheses.
Analysis of methods simulating prosthetic visual percepts through image processing.
Comparative discussion of different computer vision approaches, their strengths, and weaknesses.

Main Results:

Computer vision methods offer a promising avenue for improving visual prosthesis functionality.
Image processing techniques can optimize visual perception, enhancing the effectiveness of these devices.
The review highlights the potential of integrating advanced algorithms to overcome current limitations.

Conclusions:

Computer vision integration is crucial for advancing prosthetic vision technology.
Further research should focus on optimizing visual perception algorithms for better patient outcomes.
Developing more efficient methods for processing visual information is key to meeting user needs.