A comparative cost function approach to edge detection

Edge detection is cast as a problem in cost minimization. The concept of an edge that is based on criteria such as accurate localization, thinness, continuity, and length is described. On the basis of this description, a comparative cost function that mathematically captures the intuitive idea of an edge is formulated. The function uses information from both image data and local edge structure in evaluating the relative quality of pairs of edge configurations. The function is a linear combination of weighted cost factors. Computation of the function is performed efficiently by organizing information in the form of a decision tree. Edges are detected using a heuristic iterative search algorithm based on the comparative cost function. The detection process can be implemented largely in parallel. The usefulness of this approach to edge detection is demonstrated by showing experimental results of detected edges for both real and synthetic images