SpringerLink
Log in

A Detailed Review of Ant Colony Optimization for Improved Edge Detection

  • Conference paper
  • First Online:
Proceedings of Congress on Control, Robotics, and Mechatronics (CRM 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 364))

Included in the following conference series:

  • 207 Accesses

Abstract

Due to rapid enhancement in image processing, there is need to design and implement an improved edge detection algorithm in order to analyzing the edges of an original image. Optimization mechanism based on ant colony optimization technique has been used in present work. Research work is focused on implementation of edge detection using ant colony optimization algorithm on MATLAB and to improve the drawbacks of that algorithm and comparing it with the new improved algorithm. Present research is focused on the performance parameters, namely RMSE and PSNR. Thus, edge detection process The edge detection process considers selection of the image as input, and image is saved in a 256 color bitmap format. Then edge pixel values and generated the edges in image is calculated to generate the results with improved quality edges. Finally, comparison of the results of both algorithms and represent those results are made graphically. Proposed research is supposed to play significant role in area of image processing and quality enhancement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Eason, G., Noble, B., Sneddon, I.N.: On certain integrals of Lipschitz-Hankel type involving products of Bessel functions. Phil. Trans. Roy. Soc. London A247, 529–551 (1955)

    MathSciNet  MATH  Google Scholar 

  2. Clerk Maxwell, J.: In: A Treatise on Electricity and Magnetism, 3rd ed. vol. 2. Oxford, Clarendon, pp. 68–73. (1892)

    Google Scholar 

  3. Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: 34th International Conference Machine Learning ICML 2017, vol. 6, pp. 4457–4473. (2017)

    Google Scholar 

  4. Ansari, M.A., Anand, R.S.: Recent trends in image compression and its application in telemedicine and Teleconsuktation. In: National System Conference, pp. 59–64. (2008)

    Google Scholar 

  5. BogoToBogo Open CV3 Canny Edge Detection Homepage.: https://www.bogotobogo.com/python/OpenCV_Python/images/Canny/Canny_Edge_Detection.png. Last Accessed 24 Dec 2022

  6. https://www.researchgate.net/profile/Vijayarani-Mohan/publication/339551773/figure/fig2/AS:863426553327619@1582868340615/Different-types-of-edges-a-Step-Edge-The-intensity-of-image-abruptly-varies-from-one.png

    Google Scholar 

  7. Ghrare, S.E., Ali, M.A.M., Jumari, K., Ismail, M.: An efficient low complexity lossless coding algorithm for medical images. Am. J. Appl. Sci. 6(8), 1502–1508 (2009). https://doi.org/10.3844/ajassp.2009.1502.1508

    Article  Google Scholar 

  8. Gholizadeh-Ansari, M., Alirezaie, J., Babyn, P.: Low-dose CT denoising using edge detection layer and perceptual loss. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2019, pp. 6247–6250. (2019). https://doi.org/10.1109/EMBC.2019.8857940

  9. Abi Zeid Daou, R., El Samarani, F., Yaacoub, C., Moreau, X.: Fractional derivatives for edge detection: application to road obstacles. EAI/Springer Innov. Commun. Comput. 115–137 (2020). https://doi.org/10.1007/978-3-030-14718-1_6

  10. Zhou, J., et al.: Optical edge detection based on high-efficiency dielectric metasurface. Proc. Natl. Acad. Sci. U.S.A. 166(23), 11137–11140 (2019). https://doi.org/10.1073/pnas.1820636116

    Article  Google Scholar 

  11. Zhu, T., et al.: Generalized spatial differentiation from the spin hall effect of light and its application in image processing of edge detection. Phys. Rev. Appl. 11(3), 1 (2019). https://doi.org/10.1103/PhysRevApplied.11.034043

    Article  Google Scholar 

  12. Yuan, J., Guo, D., Zhang, G., Paul, P., Zhu, M., Yang, Q.: A resolution-free parallel algorithm for image edge detection within the framework of enzymatic numerical P systems. Molecules 24(7) (2019). https://doi.org/10.3390/molecules24071235

  13. Annamalai, J., Lakshmikanthan, C.: In: An Optimized Computer Vision and Image Processing Algorithm For Unmarked Road Edge Detection. vol. 900. Springer, Singapore (2019)

    Google Scholar 

  14. Chen, Y., Wang, D., Bi, G.: An image edge recognition approach based on multi-operator dynamic weight detection in virtual reality scenario. Cluster Comput. 22, 8069–8077 (2019). https://doi.org/10.1007/s10586-017-1604-y

    Article  Google Scholar 

  15. Chowdhary, C.L., Acharjya, D.P.: Segmentation and feature extraction in medical imaging: a systematic review. Proc. Comput. Sci. 167(2019), 26–36 (2020). https://doi.org/10.1016/j.procs.2020.03.179

    Article  Google Scholar 

  16. Lee, S., Choe, E.K., Kang, H.Y., Yoon, J.W., Kim, H.S.: The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population. Skeletal Radiol. 49(4), 613–618 (2020). https://doi.org/10.1007/s00256-019-03342-6

    Article  Google Scholar 

  17. Orujov, F., Maskeliūnas, R., Damaševičius, R., Wei, W.: Fuzzy based image edge detection algorithm for blood vessel detection in retinal images. Appl. Soft Comput. J. 94 (2020). https://doi.org/10.1016/j.asoc.2020.106452

  18. Flores-Vidal, P.A., Olaso, P., Gómez, D., Guada, C.: A new edge detection method based on global evaluation using fuzzy clustering. Soft Comput. 23(6), 1809–1821 (2019). https://doi.org/10.1007/s00500-018-3540-z

    Article  Google Scholar 

  19. Dhivya, R., Prakash, R.: Edge detection of satellite image using fuzzy logic. Cluster Comput. 22, 11891–11898 (2019). https://doi.org/10.1007/s10586-017-1508-x

  20. Moustakidis, S., Karlsson, P.: A novel feature extraction methodology using Siamese convolutional neural networks for intrusion detection. Cybersecurity 3(1) (2020). https://doi.org/10.1186/s42400-020-00056-4

  21. Cococcioni, M., Rossi, F., Ruffaldi, E., Saponara, S.: Fast deep neural networks for image processing using posits and ARM scalable vector extension. J. Real-Time Image Process. 17(3), 759–771 (2020). https://doi.org/10.1007/s11554-020-00984-x

    Article  Google Scholar 

  22. Liu, Y. et al.: A 4e-–2e- cascaded pathway for highly efficient production of H2 and H2O2 from water photo-splitting at normal pressure. Appl. Catal. B Environ. 270, 118875 (2020). https://doi.org/10.1016/j.apcatb.2020.118875

  23. Marias, K.: The constantly evolving role of medical image processing in oncology: from traditional medical image processing to imaging biomarkers and radiomics. J. Imaging 7(8) (2021). https://doi.org/10.3390/jimaging7080124

  24. Ong, J.W., Chew, W.J., Phang, S.K.: The application of image processing for monitoring student’s attention level during online class. J. Phys. Conf. Ser. 2120(1) (2021). https://doi.org/10.1088/1742-6596/2120/1/012028

  25. Panda, A., Shemshad, A.: Automated class student counting through image processing 1(1), 24–29 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chandigarh University, Mohali, Punjab, India

    Anshu Mehta & Deepika Mehta

Authors
  1. Anshu Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepika Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anshu Mehta .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Pradeep Kumar Jha

  2. Department of Mechanical Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Brijesh Tripathi

  3. Department of Mechanical and Mechatronic Engineering, UCSI University, Cheras, Kuala Lumpur, Malaysia

    Elango Natarajan

  4. Department of Computer Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mehta, A., Mehta, D. (2024). A Detailed Review of Ant Colony Optimization for Improved Edge Detection. In: Jha, P.K., Tripathi, B., Natarajan, E., Sharma, H. (eds) Proceedings of Congress on Control, Robotics, and Mechatronics. CRM 2023. Smart Innovation, Systems and Technologies, vol 364. Springer, Singapore. https://doi.org/10.1007/978-981-99-5180-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5180-2_25

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5520-6

  • Online ISBN: 978-981-99-5180-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation