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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.
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Related Experiment Video

Updated: Apr 12, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic

Jing Wang^1,2, Heng Li¹, Weizhen Fu¹

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Artificial Organs

|May 19, 2015

Summary

This summary is machine-generated.

Image processing using saliency maps improves object recognition for retinal prosthesis users. Two novel strategies, 8-4 separated pixelization and background edge extraction, enhance visual perception in simulated prosthetic vision.

Keywords:

Image processing Object recognition Retinal prosthesis Simulated prosthetic vision Visual saliency

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

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Published on: June 21, 2022

Area of Science:

Biomedical Engineering
Computer Vision
Neuroscience

Background:

Retinal prostheses aim to restore partial vision for individuals with visual impairments.
Object recognition in daily scenes is crucial for implant wearers but limited by low-resolution visual percepts.
Advanced image processing is needed to enhance visual information for users of retinal prostheses.

Purpose of the Study:

To investigate and apply image processing methods to improve object recognition for retinal prosthesis users.
To develop and evaluate two novel saliency-based image processing strategies: 8-4 separated pixelization (8-4 SP) and background edge extraction (BEE).

Main Methods:

Utilized Itti's visual saliency model to generate saliency maps.
Employed fuzzy c-means clustering for region of interest (ROI) extraction.
Applied Grabcut for proto-object generation, followed by 8-4 SP and BEE enhancement techniques.
Compared recognition accuracy against direct pixelization (DP) under varying image segmentation conditions.

Main Results:

Both 8-4 SP and BEE significantly improved object recognition accuracy compared to DP.
BEE and 8-4 SP showed higher accuracy under good and perfect segmentation conditions.
BEE demonstrated performance enhancement even under poor segmentation conditions.

Conclusions:

Saliency-based image processing strategies are beneficial for object recognition in daily scenes under simulated prosthetic vision.
These methods can aid in developing image processing modules for future retinal prostheses.
The proposed strategies offer potential for greater patient benefit by enhancing visual perception.