自动检测图像目标的形态滤波遗传算法

Optimal Morphological Filters Using Genetic Algorithm for Automatic Target Detection

提出了一种实现形态滤波器参数优化设计的遗传学习算法 (Genetic Training Algorithm forMorphological Fitters,GTAMF) .采用新的交叉与变异算子——曲面体交叉与主从式变异 ,通过优化搜索全局以获得滤波性和时效性兼优的形态滤波器参数 .实验结果表明该方法设计方便 ,实用性强且易于推广 ,对提高形态滤波性能效果明显 .分析表明 ,形态滤波器可分解为形态学运算和结构元选择两个基本问题 ,形态学运算的规则已由定义本身而确定 ,于是形态滤波器的最终滤波性能就仅仅取决于结构元的选择 .通过自适应优化训练使结构元具有图像目标的形态结构特征 ,从而赋予结构元特定的知识 ,使形态滤波过程融入特有的智能 ,以实现对复杂变化的图像具有良好的滤波性能和稳健的适应能力 .

A novel method for optimal morphological filtering parameters, namely the genetic training algorithm for morphological filters (GTAMF) is presented in this paper. GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation, to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and improves the performances of morphological filters. The operation of a morphological filter can be divided into two basic problems that include morphological operation and structuring element (SE) selection. The rules for morphological operations are predefined so the filter's properties depend merely on the selection of SE. By means of adaptive optimizing training, structuring elements possess the shape and structural characteristics of image targets, namely some information can be obtained by SE. Morphological filters formed in this way become intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.