Edge detection process plays an important role in image processing, and at its most basic level classifies image
pixels into edges and non-edge pixels. The accuracy of edge detection methods in general image processing
determines the eventual success or failure of computerized analysis procedures which follow the initial edge
detection determinations. In view of this downstream impact on pattern processing, considerable care should
be taken to improve the accuracy of the front-end edge detection. In general, edges would be considered as
abrupt changes or discontinuity in intensity of an image. Therefore, most of edge detection algorithms are
designed to capture signal discontinuities but the spatial character of especially complex edge patterns has not
received enough attention. Edges can be divided into basic patterns such as ramp, impulse, and step: different
types have different shapes and consequent mathematical properties. In this paper, the behavior of various
edge patterns, under different order derivatives in the discrete domain, are examined and analyzed to determine
how to accurately detect and localize these edge patterns, especially reducing double edge response that is one
important drawback to the derivative method. General rules about the depiction of edge patterns are proposed.
Asides from the ideal patterns already described, other pattern types, such as stair and roof, are examined to
broaden the initial analysis. Experiments conducted to test my propositions support the idea that edge patterns
are instructive in enhancing the accuracy of edge detection and localization.