Abstract
This paper describes an edge detection algorithm that employs directional templates of various sizes and rule-based decision making to assign confidence values to candidate edge pixels. High confidence values indicate a high likelihood of pixels being edge pixels, while low confidence values are associated with pixels likely to be noise. Consequently, an edge image is generated by retaining pixels associated with high confidence values. The performance of the algorithm is quantitatively evaluated on synthetic images. The evaluation concentrates on performance in the presence of noise.
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