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

TL;DR

This paper introduces an improved edge detection algorithm that directly processes colored facial and remotely sensed images using vector order statistics, reducing false and broken edges compared to traditional gray-scale conversion methods.

Contribution

The paper proposes a novel vector order statistics-based edge detection method that processes colored images directly, enhancing edge continuity and accuracy in facial and remote sensing images.

Findings

Reduces false edges in detected edge maps.

Improves edge continuity for curved lines.

Processes colored images directly without conversion.

Abstract

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.