International Journal of Electrical and Data Communication
  • Printed Journal
  • Refereed Journal
  • Peer Reviewed Journal

P-ISSN: 2708-3969, E-ISSN: 2708-3977

2023, Vol. 4, Issue 2, Part A


Optimizing image segmentation with ant colony-based techniques


Author(s): Dr. Niamh Murphy and Dr. Liam O'Leary

Abstract: Image segmentation is a crucial task in image processing and computer vision, aiming to partition an image into meaningful regions or objects for further analysis. Over the years, various optimization techniques have been employed to enhance the accuracy and efficiency of image segmentation. One of the promising methods that have emerged is the Ant Colony Optimization (ACO) algorithm, inspired by the foraging behavior of ants. This review explores the use of ACO-based techniques in image segmentation, their advantages, and limitations, as well as their applications in diverse fields such as medical imaging, remote sensing, and video analysis. The paper also discusses the hybridization of ACO with other algorithms to overcome its inherent challenges and improve performance.

Related Graphics: Click here for more related graphics

Pages: 26-31 | Views: 197 | Downloads: 79

Download Full Article: Click Here

International Journal of Electrical and Data Communication
How to cite this article:
Dr. Niamh Murphy, Dr. Liam O'Leary. Optimizing image segmentation with ant colony-based techniques. Int J Electr Data Commun 2023;4(2):26-31.
International Journal of Electrical and Data Communication
Call for book chapter