摘要
C/C复合材料的组分含量测量是进行性能分析和改进工艺的有效手段。本文作者在分析化学气相渗透工艺(CVI)制备的纯净组织C/C复合材料偏振光显微图像特点的基础上,基于模式识别原理提出了一种自适应多目标图像分割方法。根据最大类间方差准则、采用改进的Otsu算法,该系统自动计算孔隙、纤维和热解炭各相间的最佳分割阈值。实验结果表明,该方法不受C/C复合材料组分含量和组分分布形式的影响,分割质量满足定量测量的要求。
Measuring the components of carbon/carbon （C/C） composites is an effective way of performance analysis and processing optimization. In this study, a new self-adaptive algorithm of multi-object image segmentation was proposed based on the characteristic of the polarized light microscopic images of C/C composites with pure pyrocarbon fabricated by the chemistry vapor infiltration and the principle of pattern recognition. The optimal thresholds between pores, fibers and pyrocarbons were automatically computed using the improved Otsu＇s method according to the rule of the maximal variance between-class. The experimental results show that the method is effective to separate C/C composites, no matter whether the proportion or the distribution of the components is high or low, massive or scattered. The segmentation quality is fit for the further measurement of C/C composites＇ components.
出处
《复合材料学报》
EI
CAS
CSCD
北大核心
2007年第4期106-111,共6页
Acta Materiae Compositae Sinica
基金
国家杰出青年基金(G50225210)
国家自然科学基金(G50372050)
关键词
C/C复合材料
图像分析
灰度阈值法
自适应算法
最大类间方差
C/C composites
image analysis
gray threshold method
self-adaptive algorithm
maximal variance between- class
