Predicting the Particle Size Distributions of Spherical Particle Sets from Synthetic Images: A Comparison of 9 Classic Image Features

The two objectives of this paper were to demonstrate use the of the discrete element method for generating synthetic images of spherical particle configurations, and to compare the performance of 9 classic feature extraction methods for predicting the particle size distributions (PSD) from these images. The discrete element code YADE was used to generate synthetic images of granular materials to build the dataset. Nine feature extraction methods were compared: Haralick features, Histograms of Oriented Gradients, Entropy, Local Binary Patterns, Local Configuration Pattern, Complete Local Binary Patterns, the Fast Fourier transform, Gabor filters, and Discrete Haar Wavelets. The feature extraction methods were used to generate the inputs of neural networks to predict the PSD. The results show that feature extraction methods can predict the percentage passing with a root-mean-square error (RMSE) on the percentage passing as low as 1.7%. CLBP showed the best result for all particle sizes with a RMSE of 3.8 %. Better RMSE were obtained for the finest sieve (2.1%) compared to coarsest sieve (5.2%).

Investigation of the interference pattern from a spherical particle at different viewing angles

The work aim is to investigate the influence of the parameters of the laser interference method experimental setup on the obtained images. Using computer modeling based on the diffraction theory and physical experiment, the change in the parameters of the interference pattern for different viewing angles had been shown. The results can be used to develop a single algorithm for processing images of the laser interference method.