  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.
