An approach to improving edge detection for facial and remotely sensed images using vector order statistics
This addresses edge detection issues for facial and remotely sensed images, but appears incremental as it builds on existing vector order statistics methods.
The paper tackles the problem of inaccurate edge detection in facial and remotely sensed images by developing an algorithm that processes colored images directly without grayscale conversion, minimizing false and broken edges in the output edge map.
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.