Using the new method with a pre-determined, fixed Gaussian filter, the
edges shown in the result images were detected automatically after
specifying the value of one input parameter to the program. The
value of this parameters can be considered as a threshold or a
reflection of the researcher's definition of the edge he is trying
to detect. If a high valued is specified, it means that the
researcher is trying to detect the most prominent edges in the
image. But, depending on the goal of the experiment, edges less
obvious also be useful and, in this case, a low value should be
specified as the input variable. Obviously, the former case is
always easier since the S/N ratio is higher. There is always a
trade-off between the correctness and completeness of the edges
detected. Here, again, the "correctness" and "completeness" are
subjective concepts of each researcher.
Several authors at University of South Florida compared the results from edge detection methods proposed by Canny, Nalwa, Iverson, Bergholm and Rothwell. Images from four categories were used and the results are available at their web site. For comparison, one image from each of their four categories was used for testing the new method, as shown in the following examples.
From the experiment results, it can be seen that this improved
method has limited spatial resolution because of the finite size of
an LMC (the same as any other edge detector). Two side-by-side edges
of a linear feature that are very close (less than three pixels) to
each other can not be both detected satisfactorily. In some cases,
one is detected. In other cases, both are lost. When this happens,
it is better to extract the linear feature itself. For example, the
linear features whose edges may
not have been detected in Example I can be
In Example III, at the tip of one of the leaves in the upper left part of the result images, there is a highlighted pixel segment that corresponds to a not-very-obvious edge in the original image. If a threshold is set for the contrast of the edges detected, results can be obtained that does not include such "run-off" edges.
Similar results from these images can be obtained by using EdgeDetector.
Edge Detection Home Page
Results from the head image || Results from the Lena image
Download EdgeDetector || Download Synthetic Test Images