  Since edge detection is in the forefront of image processing for object
detection, it is crucial to have a good understanding of edge detection
algorithms. The reason for this is that edges form the outline of an object. An
edge is the boundary between an object and the background, and indicates the
boundary between overlapping objects. This means that if the edges in an image
can be identified accurately, all of the objects can be located and basic
properties such as area, perimeter, and shape can be measured. Since computer
vision involves the identification and classification of objects in an image,
edge detection is an essential tool. We tested two edge detectors that use
different methods for detecting edges and compared their results under a
variety of situations to determine which detector was preferable under
different sets of conditions.
