论文标题
叠加纳米颗粒的SEM图像的叠加和串扰图像识别方法在单晶硅表面具有高聚集度
A Superimposed Divide-and-Conquer Image Recognition Method for SEM Images of Nanoparticles on The Surface of Monocrystalline silicon with High Aggregation Degree
论文作者
论文摘要
通常通过手动方法计数硅晶体的SEM图像中的纳米颗粒大小和分布信息。自动机器识别的实现在材料科学中很重要。本文提出了一种叠加分区图像识别方法，以实现硅晶体纳米粒子SEM图像的自动识别和信息统计。特别是对于硅晶体尺寸的复杂且高度聚集的特征，给出了基于形态学处理的准确识别步骤和轮廓统计方法。该方法具有在不同的SEM拍摄条件下识别单晶硅表面纳米颗粒图像的技术参考值。此外，它在识别准确性和算法效率方面都优于其他方法。
The nanoparticle size and distribution information in the SEM images of silicon crystals are generally counted by manual methods. The realization of automatic machine recognition is significant in materials science. This paper proposed a superposition partitioning image recognition method to realize automatic recognition and information statistics of silicon crystal nanoparticle SEM images. Especially for the complex and highly aggregated characteristics of silicon crystal particle size, an accurate recognition step and contour statistics method based on morphological processing are given. This method has technical reference value for the recognition of Monocrystalline silicon surface nanoparticle images under different SEM shooting conditions. Besides, it outperforms other methods in terms of recognition accuracy and algorithm efficiency.
