炭素制品X射线图像的缺陷特征分析与选择

针对炭素制品X射线检测图像的特点，对缺陷及其特征提取与选择技术进行了研究。分析了炭素制品生产中易产生的缺陷类型及缺陷的成像特征，在此基础上，从缺陷样本中提取了19个特征值。以特征组合分类能力数学模型为适应度函数，设计了基于遗传算法的特征选择策略，实现了对缺陷原始特征量的优化选择。利用BP神经网络分类器及选择的特征值对缺陷进行了模式分类。研究结果表明，提出的选择方法是比较有效的，可以用于缺陷的识别与分类。

Flaw Feature Analysis and Selection of X-ray Images of Carbon Products

Regarding the characteristic of X-ray detection images of carbon material, flaw feature extraction and selection techniques are studied. Defect style and imaging character of carbon product that easy create in the course of produce are analyzed, based on that, nineteen features are extracted from flaw stylebook. Mathematics model of feature combination classification is regarded as fitness function, optimal selection of original flaw feature is realized with feature selection strategy based on genetic algorithm. Pattern classification of flaw is carried out with BP neural network and the feature selected. Experiment results show that1 the method of feature selection is relatively effective, and it could be used for the recognition and classification of flaw.