Edge pattern analysis for edge detection and localization

Abstract

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.