Spot image ablated by femtosecond laser segmentation and feature clustering after dimension reduction reconstruction

Abstract

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.