An edge in an image is a boundary or contour at which a significant change occurs in some physical aspect of an image, such as the surface reflectance, illumination, or the distances of the visible surfaces from the viewer. Changes in physical aspects manifest themselves in a variety of ways, including changes in intensity, color, and texture. In our discussion, we are concerned only with the changes in image intensity.
Detecting edges is very useful in a number of contexts. For example, in a typical image understanding task such as object identification, an essential step is to segment an image into different regions corresponding to different objects in the scene. Edge detection is often the first step in image segmentation. As another example, one approach to the development of a low bit-rate image coding system is to code only the detected edges. it is well known that an image that consists of only edges is highly intelligible.
The significance of a physical change in an image depends on the application; an intensity change that would be classified as an edge in some applications might not be considered an edge in other applications. In an object identification system, an object’s boundaries may be sufficient for identification and contours that represent additional details within the object may not be considered edges. An edge cannot be defined, then, outside of the context of an application. Nevertheless, edge detection algorithms that detect edges that are useful in a broad set of applications have been developed. In this section, we discuss some of the more representative edge detection algorithms.