Artificial Intelligence in Image Recognition: Context of Machine Vision

Year : 2025 | Volume : 12 | Issue : 03 | Page : 01 06
    By

    Atish Kumar Pandey,

  • Manpreet Kaur,

  • Kulwinder Kaur,

  • Suman Rani,

  1. Student, Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India
  2. Assistant Professor, Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India
  3. Assistant Professor, Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India
  4. Assistant Professor, Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India

Abstract

The machine learning discipline is as old as decades, but some problems such as image recognition, location detection, image classification, image generation, speech recognition, and natural language processing cannot be solved. Image classification studies are another basic, most classic and essential line of research in deep learning. Computer intelligent recognition of the images technology has enabled a gradual reaction (updating) to foreign measurement trends, which promotes advancement of different areas of investigation. The broad usage of the image processing technology is a machine-learning-based approach that provides solutions in different spheres by carrying out the operations on features extraction, classification tasks, segmentation functions, and recognition tasks. Image recognition technology has been applied in the transportation industry in license plate recognition. These identify plates in a complex background and segment the characters and identify them to produce automatic non-license plate algorithms and the greater feature is that it enhances speed in detecting license plates. License plate training sample set diversity and the high generation rates make strong classifier training possible. In addition to license plate recognition accuracy, the anti-interference capability is also significantly enhanced by means of the deployment of genetic algorithm optimization in the BP neural network classification framework.

Keywords: Artificial intelligence (AI), machine learning (ML), deep learning, neural networks, computer vision, image processing, pattern recognition, feature extract

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
Atish Kumar Pandey, Manpreet Kaur, Kulwinder Kaur, Suman Rani. Artificial Intelligence in Image Recognition: Context of Machine Vision. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(03):01-06.
How to cite this URL:
Atish Kumar Pandey, Manpreet Kaur, Kulwinder Kaur, Suman Rani. Artificial Intelligence in Image Recognition: Context of Machine Vision. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(03):01-06. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=227979


References

  1. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017 May 24; 60(6): 84–90.
  2. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4; 1–14.
  3. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016; 770–778.
  4. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2015; 1–9.
  5. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, PMLR. 2019 May 24; 6105–6114.
  6. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J. An image is worth 16×16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22; 1–22.
  7. Cao B, Araujo A, Sim J. Unifying deep local and global features for image search. In European conference on computer vision. Cham: Springer International Publishing; 2020 Aug 23; 726–743.
  8. Wei XS, Song YZ, Mac Aodha O, Wu J, Peng Y, Tang J, Yang J, Belongie S. Fine-grained image analysis with deep learning: A survey. IEEE Trans Pattern Anal Mach Intell. 2021 Nov 9; 44(12): 8927–48.
  9. Patil A. Image recognition using machine learning. Available at SSRN 3835625. 2021 Feb 1.
  10. Li Y. Research and application of deep learning in image recognition. In 2022 IEEE 2nd international conference on power, electronics and computer applications (ICPECA). 2022 Jan 21; 994–999.

Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 26/05/2025
Accepted 23/06/2025
Published 19/09/2025
Publication Time 116 Days


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