一种改进谱聚类的机体损伤图像过渡区提取方法

Airframe Damage Image Transition Region Extraction Method Based on Improved Spectral Clustering

为了利用机体损伤区域图像支持飞机智能维修,提出一种改进谱聚类的机体损伤过渡区提取方法.在分析机体损伤邻接区域特点的基础上,以多维灰度熵值作为相似性度量构建样本点谱图实现了谱图权重的降维运算;并以统计分组中首个极小值点所对应的权重为临界进行聚类;通过对谱聚类方法中的关键环节特征值计算、特征向量选取进行改进,避免了因特征向量的取舍而造成的信息丢失.最后应用机体损伤图像实例验证了该方法的有效性.

In order to realize aircraft intelligent maintenance technology by using images of airframe damage region, an airframe damage transition region extraction method based on improved spectral clustering is proposed. The characteristic of airframe damage neighboring region was analyzed, on the basis of which multi-dimension gray entropy value was used as similarity measurement. Spectra has been constructed and dimension of weights has been reduced. The critical value of cluster was the weight which was corresponding to the first minimum point of statistical group. By improving eigenvalue calculation and eigenvector selection of spectral clustering method, information loss causing by these two steps is avoided. Finally, validation verification was performed using several airframe damage images.