Affiliation:
1. Department of Computer, Collage of Basic Education, University of AL-Mustansiriyah, Baghdad, Iraq
Abstract
Edge detection is a fundamental image processing technique used to spot sudden shifts in color or intensity in image. It is utilized to detect and highlight boundaries between various items or regions in image, as well as to detect features such as corners, circles and lines. Edge detection approaches typically work by applying a filter to an image to detect areas where the image undergoes an immediate shift in magnitude. Applications for edge detection techniques include recognizing objects, healthcare images, and background segmentation. Many techniques have been presented based on the classical approaches (such as Sobel, Prewitt, and Roberts, Canny, Laplacian of Gaussian (LOG), etc.) and soft computing approaches (SCA), which are the two main approaches for detection of edge. This paper provides an overview of studies carried out on edge detection using various approaches. That will assist brand-new researchers in learning about these techniques and selecting one from among them to evolve or improve according to their field of study.
Reference48 articles.
1. I. K. Ajlan, A. A. Daleh Al-magsoos and H. G. Murad, 'A Comparative Study of Edge Detection Techniques in Digital,' Journal of Mechanical Engineering Research and Developments, vol. 44, pp. 109-119, 2021.
2. J. Jing, S. Liu, G. Wang, W. Zhang and C. Sun, 'Recent advances on image edge detection: A comprehensive review,' Neurocomputing, vol. 503, pp. 259-271, 7 September 2022.
3. A. Dr. S.Vijayarani, 'A Performance Comparison of Edge Detection Techniques for Printed and Handwritten,' International Journal of Innovative Research in Computer, vol. 4, no. 5, pp. 8327-8337, May 2016.
4. D. S.Lakshmi, 'A study of Edge Detection Techniques for Segmentation,' International Journal of Computer Applications (IJCA), Special Issue on CASCT, pp. 35-41, 20 August 2010.
5. S. Kaur and I. Singh, 'Comparison between Edge Detection Techniques,' International Journal of Computer Applications, vol. 145, no. 15, pp. 15-18, July 2016.