Spot image ablated by femtosecond laser segmentation and feature clustering after dimension reduction reconstruction
Spot image ablated by femtosecond laser segmentation and feature clustering after dimension reduction reconstruction
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DOI：
10.1016/j.ijleo.2018.03.027
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发表时间：
2018-07
期刊：
影响因子：
3.1
通讯作者：
Wang Fubin;Liu Yang;Wu Chen;Xianbang Chen;Kai Zeng
中科院分区：
文献类型：
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作者：
Wang Fubin;Liu Yang;Wu Chen;Xianbang Chen;Kai Zeng
当单晶硅材料被飞秒激光烧蚀时，也会产生等离子体衍生发光。通过采集等离子体光斑图像，分析光斑图像的特征，为激光烧蚀功率的分类和烧蚀过程的参数优化提供依据。针对等离子体斑点边缘与背景区域对比度不明显、信噪比较低的特点，通过比较传统的最大类平方误差方法(OTSU)和主成分分析方法(PCA)，分析了弱小斑点目标的分割效率。实验研究表明，采用主成分分析方法分割的斑点图像提取的多个几何特征较为一致，但传统的最大类间方差法将烧蚀过渡区和局部晕环分割为目标，导致提取的斑点几何特征间的离散性较大，不利于烧蚀过程参数的特征分析和识别。在此基础上，提取不同烧蚀功率下光斑图像的亮度，结合光斑图像的像素面积、周长、长短轴和长短轴比，建立六维特征矩阵对激光烧蚀功率进行分类。针对等离子体斑点序列图像采集的数据量大的特点，采用流形学习算法对特征矩阵进行降维。分别选取10 mW、20 mW和50 mW烧蚀功率的SPOT图像各100帧构建6维特征矩阵，对比使用3种流形学习算法实现矩阵降维和散点图重建，观察了3种烧蚀功率下SPOT图像在三维空间和二维平面上的特征分布，发现LPP(局部保持投影)算法和LTSA(线性局部切线空间对齐)算法对降维特征点的聚类效果较好，可用于不同烧蚀功率下SPOT图像的分类。
When monocrystalline silicon material is ablated by femtosecond laser, plasma derived luminescence also occurs. By collecting plasma spot image and analyzing the feature of spot image, which can be used for the classification of laser ablation power and the parameters optimization of ablation process. Considering that the contrast between edge and background area of plasma spot is not obvious and the signal to noise ratio is lower, we analyzes the segmentation efficiency for dim spot target by comparing the traditional Otsu (maximum class square error method) and PCA(Principal Component Analysis). The experimental study shows that the extracted multiple geometric features of spot image segmented by PCA method are more consistent; however, the ablation transition zone and part halo are segmented into target by traditional Otsu, which leads to the larger dispersion among extracted geometric features of spot, and it is also not beneficial for feature analysis and recognition of ablation process parameters. Further, the brightness of spot image under different ablation power was extracted, combining with pixel area, perimeter, long and short axis and long and short axis ratio of spot image, a six-dimensional feature matrix is built to classify the laser ablation power. Because of the large amount of data collected plasma spot sequence image, the manifold learning algorithm is used to reduce the dimension of feature matrix. Each spot image with 10mW, 20mW and 50mW ablation power is selected respectively 100 frames to build the six-dimensional feature matrix, three manifold learning algorithms are used in contrast to realize matrix dimensionality reduction and scatter plot reconstruction, then the feature distributions of spot image ablated by three kinds of ablation power are observed in three-dimensional space and two-dimensional plane, we find that the clustering effect for decreased dimension feature points using LPP(Locality preserving projections) algorithm and LLTSA(Linear Local Tangent Space Alignment) are better, they can be used to classify the spot image under different ablation power.