A COST MINIMIZATION APPROACH TO EDGE-DETECTION USING SIMULATED ANNEALING

In this paper, we cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The function is a linear sum of weighted cost factors. The cost factors capture desirable characteristics of edges such as accuracy in localization, thinness, and continuity. Edges are detected by finding the edge configurations that minimize the cost function. We give a mathematical description of edges and analyze the cost function in terms of the characteristics of the edges in minimum cost configurations. Through the analysis, we provide guidelines on the choice of weights to achieve certain characteristics of the detected edges. The cost function is minimized by the stimulated annealing method. We present a novel set of strategies for generating candidate states and devise a suitable temperature schedule. Experimental results, which verify the usefulness of our cost minimization approach to edge detection, are given.