A Survey on Various Approaches for Edge Detection

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.