Literature DB >> 34318520

The application of computer vision to visual prosthesis.

Jing Wang1,2, Haiyi Zhu1, Jianyun Liu1, Heng Li3, Yanling Han1, Ruyan Zhou1, Yun Zhang1,2.   

Abstract

A visual prosthesis is an auxiliary device for patients with blinding diseases that cannot be treated with conventional surgery or drugs. It converts captured images into corresponding electrical stimulation patterns, according to which phosphenes are generated through the action of internal electrodes on the visual pathway to form visual perception. However, due to some restrictions such as the few implantable electrodes that the biological tissue can accommodate, the induced perception is far from ideal. Therefore, an important issue in visual prosthesis research is how to detect and present useful information in low-resolution prosthetic vision to improve the visual function of the wearer. In recent years, with the development and broad application of computer vision methods, researchers have investigated the possibility of their utilization in visual prostheses by simulating prosthetic visual percepts. Through the optimization of visual perception by image processing, the efficiency of visual prosthesis devices can be further improved to better meet the needs of prosthesis wearers. In this article, recent works on prosthetic vision centering on implementing computer vision methods are reviewed. Differences, strengths, and weaknesses of the mentioned methods are discussed. The development directions of optimizing prosthetic vision and improving methods of visual perception are analyzed.
© 2021 International Center for Artificial Organs and Transplantation and Wiley Periodicals LLC.

Entities:  

Keywords:  computer vision; image processing; machine learning; simulated prosthetic vision; visual prosthesis

Mesh:

Year:  2021        PMID: 34318520     DOI: 10.1111/aor.14022

Source DB:  PubMed          Journal:  Artif Organs        ISSN: 0160-564X            Impact factor:   3.094


  1 in total

1.  An automated solid waste detection using the optimized YOLO model for riverine management.

Authors:  Nur Athirah Zailan; Muhammad Mokhzaini Azizan; Khairunnisa Hasikin; Anis Salwa Mohd Khairuddin; Uswah Khairuddin
Journal:  Front Public Health       Date:  2022-08-12
  1 in total

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