skip to main content
10.1145/3653081.3653087acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiotaaiConference Proceedingsconference-collections
research-article

National Flags Recognition Based on Image Processing

Authors Info & Claims
Published:03 May 2024Publication History

ABSTRACT

By using Matlab digital image processing tool, the method of national flags image recognition and detection is obtained. Firstly, the original color image is grayed, and then, the Gaussian mean filter is used to suppress the noise as much as possible without losing most details. Histogram equalization algorithm is used for image enhancement. Then, through multiple clustering segmentation method, more obvious features of the image are obtained. Finally, feature matching algorithm is used to detect and recognize the national flags image. This algorithm has the characteristics of high robustness and high accuracy. Through Matlab simulation experiment, this algorithm can complete the recognition of national flags image.

References

  1. Gil Pablo. “Short project-based learning with MATLAB applications to support the learning of video-image processing,” Journal of Science Education and Technology, 26(5), 508-518, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  2. A Nourdine, M Gomez, S Javier. “Web and MATLAB Based Platform for UAV Flight Management and Multispectral Image Processing,” Sensors, 22(11), 4243, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  3. G K Ijemaru, A O Nwajana, E Oleka, “Image processing system using MATLAB-based analytics,” Bulletin of Electrical Engineering and Informatics, 10 (5), 2566-2577, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  4. T Mangayarkarasi, S S Anusha, P R Pranavikha. “Covid-19 Mask Detection Using Matlab Based Image Processing,” Elementary Education Online, 20(1), 5016-5023, 2021.Google ScholarGoogle Scholar
  5. Sukanya, G Dubey. “Matlab Based Road Detection from Satellite Images using Image Processing,” Internationnal Journal of Research in Electronics and Computer Engineering, 7(4), 2019.Google ScholarGoogle Scholar
  6. G J Dolecek, N Cho. “Advances in image processing using machine learning techniques,” IET Signal Processing, 16(6), 615-618, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  7. L P Bass, Y A Plastinin, Y Skryabysheva. “Machine Learning In Problems Involved In Processing Satellite Images,” Measurement Techniques, 63(12), 950-958, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  8. L D Hanh, |D Bao. “Autonomous lemon grading system by using machine learning and traditional image processing,” International Journal on Interactive Design and Manufacturing, 17(1), 445-452, 2023.Google ScholarGoogle ScholarCross RefCross Ref
  9. R Ruslan, S Khairunniza-Bejo, M Jahari, M Ibrahim. Weedy Rice Classification Using Image Processing and a Machine Learning Approach,” Agriculture, 12(645), 645, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  10. T Fukuoka, M Fujiu. “Detection of Bridge Damages by Image Processing Using the Deep Learning Transformer Model,” Buildings, 13(788), 788, 2023.Google ScholarGoogle ScholarCross RefCross Ref
  11. B Ninja, H M Kumar. “Deep learning based image processing solutions in food engineering: A review,” Agricultural Reviews, 43(3), 267-277, 2022.Google ScholarGoogle Scholar
  12. H Ding; M A Latif, Z Zia, “Facial Mask Detection Using Image Processing with Deep Learning,” Mathematical Problems in Engineering, 2022, 1-10 (2022).Google ScholarGoogle Scholar
  13. S M Myint, M M Myint; A A Cho. “National Flags Recognition Based on Principal Component Analysis,” Journal of Trend in Scientific Research and Development, 3(5), 2019.Google ScholarGoogle Scholar
  14. J H Ford, C Wilcox. “Shedding light on the dark side of maritime trade - A new approach for identifying countries as flags of convenience,” Marine Policy. 99(C), 298-303, 2019.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. National Flags Recognition Based on Image Processing

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      IoTAAI '23: Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
      November 2023
      902 pages
      ISBN:9798400716485
      DOI:10.1145/3653081

      Copyright © 2023 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 May 2024

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited
    • Article Metrics

      • Downloads (Last 12 months)2
      • Downloads (Last 6 weeks)2

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format