AN EDGE-DETECTION TECHNIQUE USING GENETIC ALGORITHM-BASED OPTIMIZATION
AN EDGE-DETECTION TECHNIQUE USING GENETIC ALGORITHM-BASED OPTIMIZATION
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DOI：
10.1016/0031-3203(94)90003-5
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发表时间：
1994-09-01
影响因子：
8
通讯作者：
POTTER, WD
中科院分区：
文献类型：
--
作者：
BHANDARKAR, SM;ZHANG, YQ;POTTER, WD
在本文中，我们提出了一种基于遗传算法的边缘检测优化技术。边缘检测问题被表述为选择最小成本边缘配置的问题之一。边缘配置被视为二维染色体，其适应度值与其成本成反比。描述了二维染色体表示背景下交叉和变异算子的设计。利用局部边缘结构知识的知识增强变异算子被证明可以实现快速收敛。讨论了元级算子和策略（例如精英策略、工程条件算子以及边缘检测背景下突变和交叉率的自适应）的结合，并证明可以提高收敛速度。在合成图像和自然图像上测试了具有各种元级算子组合的遗传算法。基于遗传算法的成本最小化技术的性能与基于局部搜索和基于模拟退火的成本最小化方法进行了定性和定量比较。基于遗传算法的技术在噪声鲁棒性、收敛速度和最终边缘图像的质量方面表现得非常好。
In this paper we present a genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one 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 in the context of the two-dimensional chromosomal representation 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 such as the elitism strategy, the engineered conditioning operator and adaptation of mutation and crossover rates in the context of edge detection are discussed and are shown to improve the convergence rate. The genetic algorithm with various combinations of meta-level operators is tested on synthetic and natural images. The performance of the genetic algorithm-based cost minimization technique is compared both qualitatively and quantitatively with local search-based and simulated annealing-based cost minimization approaches. The genetic algorithm-based technique is shown to perform very well in terms of robustness to noise, rate of convergence and quality of the final edge image.