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ORIGINAL RESEARCH article

Front. Mater.
Sec. Smart Materials
Volume 11 - 2024 | doi: 10.3389/fmats.2024.1431179
This article is part of the Research Topic Biological-Inspired Artificial Intelligent Systems: State and Perspectives View all 4 articles

Applications of artificial intelligence and machine learning in image processing

Provisionally accepted
Xu Pingyuan Xu Pingyuan 1Wang Jinyuan Wang Jinyuan 2yu jiang yu jiang 2xiangbing Gong xiangbing Gong 2*
  • 1 Zhejiang Infrastructre Construction Grop, HangZhou, China
  • 2 School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, Hunan Province, China

The final, formatted version of the article will be published soon.

    With the rapid development of artificial intelligence and machine learning technology, image processing technology based on artificial intelligence and machine learning has been applied in various fields, which effectively solves the multi-classification problem of similar targets in traditional image processing technology. This paper summarizes the various algorithms of artificial intelligence and machine learning in image processing, the development process of neural network model, the principle of model and the advantages and disadvantages of different algorithms, and introduces the specific application of image processing technology based on these algorithms in different scientific research fields.Finally, the application of artificial intelligence and machine learning in image processing is summarized and prospected, in order to provide some reference for researchers who used artificial intelligence and machine learning for image processing in different fields.

    Keywords: Artificial intelligence and machine learning, Artificial intelligence algorithm, neural network model, Image processing and application, deep learning

    Received: 11 May 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Pingyuan, Jinyuan, jiang and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: xiangbing Gong, School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, 410004, Hunan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.