Edge detection: a review of dissimilarity evaluations and a proposed normalized measure

Abstract

In digital images, edges characterize object boundaries, so edge detection remains a crucial stage in numerous applications. To achieve this task, many edge detectors have been designed, producing different results, with various qualities of segmentation. Indeed, optimizing the response obtained by these detectors has become a crucial issue, and effective contour assessment assists performance evaluation. In this paper, several referenced-based boundary detection evaluations are detailed, pointing out their advantages and disadvantages, theoretically and through concrete examples of image edges. Then, a new normalized supervised edge map quality measure is proposed, comparing a ground truth contour image, the candidate contour image and their associated spatial nearness. The effectiveness of the proposed distance measure is demonstrated theoretically and through several experiments, comparing the results with the methods detailed in the state-of-the-art. In summary, compared to other boundary detection assessments, this new method proved to be a more reliable edge map quality measure.