Edge Detection Techniques-An Overview

TLDR

Edge detection in computer vision localizes significant grey‑level variations and is essential for 3D reconstruction, motion analysis, recognition, enhancement, registration, and compression, yet it requires smoothing and differentiation—an ill‑conditioned process that loses information—making it hard to devise a universally effective algorithm, so many detectors with varied mathematical properties have been developed over time. This paper reviews the current state of understanding of edge detection. The authors outline an overview of edge detection research, covering edge definitions, detector properties, detection methodology, the interplay between edges and detectors, and implementations of existing detectors.

Abstract

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. This information is very useful for applications in 3D reconstruction, motion, recognition, image enhancement and restoration, image registration, image compression, and so on. Usually, edge detection requires smoothing and differentiation of the image. Differentiation is an ill-conditioned problem and smoothing results in a loss of information. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research in edge detection: edge definition, properties of detectors, the methodology of edge detection, the mutual influence between edges and detectors, and existing edge detectors and their implementation.