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<p>The authors 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. The authors 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, guidelines are provided on the choice of weights to achieve certain characteristics of the detected edges. The cost function is minimized by the simulated annealing method. A set of strategies is presented for generating candidate states and to devise a suitable temperature schedule.</p>
picture processing; pattern recognition; cost minimization; edge detection; simulated annealing; weighted cost factors; localization; thinness; pattern recognition; signal processing; simulated annealing

E. Delp, S. Gelfand and H. Tan, "A Cost Minimization Approach to Edge Detection Using Simulated Annealing," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 3-18, 1992.
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