Edges are commonly defined as significant local changes in an image. Edge provides an indication of the physical extent of objects in the image. Edge detection is viewed as an information reduction process that provides boundary information of regions by filtering out unnecessary information for the next steps of processes in a computer vision system. Thus, edge detection is one of the most essential steps for extracting structural features for human and machine perception. The success of high‐level computer vision processes heavily relies on the good output from the lower level processes such as edge detection. Many edge detection algorithms have been proposed in the last 50 years. This article presents the fundamental theories and the important edge detection techniques for grayscale, color, and range images.