Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Modeling of the Potential Dependence on the Permittivity at the Metal – Dielectric Medium Interface Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Review of Modern Technologies of Computer Vision

Authors : Ekaterina Bezuglova, Andrey Gladkov, Georgy Valuev

Published in: Current Problems in Applied Mathematics and Computer Science and Systems

Publisher: Springer Nature Switzerland

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Today, the use of artificial intelligence technologies is becoming more and more popular. Scientific and technological progress contributes to increasing the power of hardware, as well as obtaining effective methods for implementing methods such as machine learning, neural networks, and deep learning. This created the possibility of creating effective methods for recognizing images and video data, which is what computer vision is. At the time of 2022, a huge number of methods, technologies, and techniques for using computer vision were received, in this paper a study was conducted on the use of computer vision in 2022. Results were obtained on the decrease in the popularity of computer vision in the scientific community, its introduction into industry, medicine, zoology and human social life, the most popular method of computer vision is the ResNet neural network model.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Model of Error Correction Device in RNS-FRNN
next chapter Discrete Neural Network of Bidirectional Associative Memory
Literature
1.
Mitchell, T., Buchanan, B., DeJong, G., Dietterich, T., Rosenbloom, P., Waibel, A.: Machine learning. Annu. Rev. Comput. Sci. 4(1), 417–433 (1990)CrossRef
2.
Shapiro, L.G., Stockman, G.C.: Computer Vision, vol. 3. Prentice Hall, Upper Saddle River (2001)
3.
Lei, Y., et al.: Development of a slow loris computer vision detection model. Animals 12(12), 1553 (2022)CrossRef
4.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
5.
Yao, J., Qi, J., Zhang, J., Shao, H., Yang, J., Li, X.: A real-time detection algorithm for Kiwifruit defects based on YOLOv5. Electronics 10(14), 1711 (2021)CrossRef
6.
7.
Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W.C., Wang, C.B., Bernardini, S.: The COVID-19 pandemic. Crit. Rev. Clin. Lab. Sci. 57(6), 365–388 (2020)CrossRef
8.
Crespo, F., Crespo, A., Sierra-Martínez, L.M., Peluffo-Ordóñez, D.H., Morocho-Cayamcela, M.E.: A computer vision model to identify the incorrect use of face masks for COVID-19 awareness. Appl. Sci. 12(14), 6924 (2022)
9.
Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533 (2016)
10.
Batagelj, B., Peer, P., Štruc, V., Dobrišek, S.: How to correctly detect face-masks for covid-19 from visual information? Appl. Sci. 11(5), 2070 (2021)CrossRef
11.
Ge, S., Li, J., Ye, Q., Luo, Z.: Detecting masked faces in the wild with LLE-CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2682–2690 (2017)
12.
Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: Retinaface: single-shot multi-level face localisation in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5203–5212 (2020)
13.
14.
Zhang, H., et al.: Resnet: split-attention networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2736–2746 (2022)
15.
Amari, S.I.: Backpropagation and stochastic gradient descent method. Neurocomputing 5(4–5), 185–196 (1993)CrossRefMATH
16.
Dehghani, M., Gritsenko, A., Arnab, A., Minderer, M., Tay, Y.: Scenic: a JAX library for computer vision research and beyond. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21393–21398 (2022)
17.
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:​2010.​11929 (2020)
18.
19.
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR, July 2021
20.
Tolstikhin, I.O., et al.: MLP-mixer: an all-MLP architecture for vision. Adv. Neural. Inf. Process. Syst. 34, 24261–24272 (2021)
21.
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1–67 (2020)MathSciNetMATH
22.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:​1810.​04805 (2018)
23.
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: Vivit: a video vision transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6836–6846 (2021)
24.
Tay, Y., et al.: Omninet: omnidirectional representations from transformers. In: International Conference on Machine Learning, pp. 10193–10202. PMLR, July 2021
25.
26.
27.
Su, J., Zhou, C., Chen, H., Xia, N., Shi, Z.: The physical and mechanical properties for flexible biomass particles using computer vision. Fuel 315, 123278 (2022)CrossRef
28.
Kroetsch, D., Wang, C.: Particle size distribution. Soil Sampling Methods Anal. 2, 713–725 (2008)
29.
Toolbox, S.M.: Matlab. Mathworks Inc. (1993)
30.
Stefenon, S.F., et al.: Classification of insulators using neural network based on computer vision. IET Gener. Transm. Distrib. 16(6), 1096–1107 (2022)CrossRef
31.
Bradski, G., Kaehler, A.: OpenCV. Dr. Dobb’s J. Softw. Tools 3, 120 (2000)
32.
33.
Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234–1240 (2020)CrossRef
34.
Patrício, D.I., Rieder, R.: Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput. Electron. Agric. 153, 69–81 (2018)CrossRef
35.
36.
Navarro Soto, J., Satorres Martínez, S., Martínez Gila, D., Gómez Ortega, J., Gámez García, J.: Fast and reliable determination of virgin olive oil quality by fruit inspection using computer vision. Sensors 18(11), 3826 (2018)
37.
Sherine, A., Peter, G., Stonier, A.A., Praghash, K., Ganji, V: CMY color spaced-based visual cryptography scheme for secret sharing of data. Wirel. Commun. Mob. Comput. (2022)
Metadata
Title
Review of Modern Technologies of Computer Vision
Authors
Ekaterina Bezuglova
Andrey Gladkov
Georgy Valuev
Copyright Year
2023
Book
Current Problems in Applied Mathematics and Computer Science and Systems

Print ISBN: 978-3-031-34126-7

Electronic ISBN: 978-3-031-34127-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-34127-4_31

Premium Partners

    Image Credits