A Superimposed Divide-and-Conquer Image Recognition Method for SEM Images of Nanoparticles on The Surface of Monocrystalline silicon with High Aggregation Degree
This addresses the need for automated analysis in materials science, specifically for SEM images of nanoparticles on monocrystalline silicon surfaces, though it appears incremental as it builds on existing morphological processing techniques.
The paper tackles the problem of manually counting nanoparticle size and distribution in SEM images of silicon crystals by proposing a superimposed divide-and-conquer image recognition method, achieving automatic recognition with improved accuracy and efficiency compared to other methods.
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.