摘要
针对炭素制品X光图像的特点,对其缺陷的提取技术进行了研究,提出了基于迭代的阈值构造方法和基于数学形态学的边缘提取算法。为快速准确地提取缺陷,设计了目标边界提取算法和基于小波变换的图像增强算法,实现了原始图像中目标区域的增强及其背景的去除。在此基础上,为排除噪声干扰的影响,采用数学形态学和迭代阈值分割相结合的方法从目标区域中提取出缺陷区域,并在迭代阈值分割的基础上,利用基于数学形态学的边缘提取算法提取了缺陷的边缘。实验结果表明,该法很好地实现了缺陷区域及其边缘的自动提取,且受噪声影响很小,为进一步的缺陷特征参量的提取与选择奠定了良好的基础。
Defect extraction techniques are studied regarding the characteristic of X-ray images of carbon product, and threshold-construction method based on iteration and edge-extraction algorithm based on mathematical morphology are advanced. In order to extract defects quickly and exactly, target boundary extraction algorithm and image enhancement algorithm based on wavelet transform are proposed, background removal and enhancement of object region are implemented successfully. Based on this method, combining mathematical morphology and iteration threshold segmentation is adopted to extract defect in order to eliminate the noise disturbance, and with iteration threshold segmentation, defect edge-extraction is realized based on edge-extraction algorithm of mathematical morphology. The experimental results indicate that the method can achieve automatic extraction of defect region and edge with weak noise disturbance , which lays a good foundation for flaw feature parameter extraction and choice.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2006年第7期1016-1020,共5页
Acta Optica Sinica
基金
湖南省教育厅重点科研项目(03A052)
企业横向项目(G1999064910)资助课题
