  This paper presents an improved edge detection algorithm for facial and
remotely sensed images using vector order statistics. The developed algorithm
processes colored images directly without been converted to gray scale. A
number of the existing algorithms converts the colored images into gray scale
before detection of edges. But this process leads to inaccurate precision of
recognized edges, thus producing false and broken edges in the output edge map.
Facial and remotely sensed images consist of curved edge lines which have to be
detected continuously to prevent broken edges. In order to deal with this, a
collection of pixel approach is introduced with a view to minimizing the false
and broken edges that exists in the generated output edge map of facial and
remotely sensed images.
