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• W2125767281 abstract "In this paper we present a genetic algorithm-based cost minimization technique for edge detection. Edge detection is formulated as a process of choosing a minimum cost edge configuration. The edge configurations are viewed as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators is described. The knowledge-augmented mutation operator which exploits knowledge of the local edge structure is shown to result in rapid convergence. The incorporation of meta-level operators and strategies in the context of edge detection are discussed and are shown to improve the convergence rate." @default.
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• W2125767281 date "2005-08-24" @default.
• W2125767281 modified "2023-09-26" @default.
• W2125767281 title "A genetic algorithm-based edge detection technique" @default.
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• W2125767281 doi "https://doi.org/10.1109/ijcnn.1993.714352" @default.
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