An approach to improving edge detection for facial and remotely sensed images using vector order statistics

This paper presents an improved edge detection algorithm for facial and remotely sensed images using vector order statistics. The developed algorithm processes coloured images directly without been converted to grey scale. A number of the existing algorithms converts the coloured images into grey 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.