Characterizing powder materials using keypoint-based computer vision methods

Abstract

We applied the bag of visual words model for visual texture to a dataset of realistic powder micrograph images drawn from eight closely related particle size distributions. We found that image texture based powder classification performance saturates at 89±3% with 640 training images (80 images per class). This classification accuracy is comparable to classification using conventional segmentation-based particle size analysis. Furthermore, we found that particle size distributions obtained via watershed segmentation are generally not statistically equivalent to the ground truth particle size distributions, as quantified by the two-sample Kolmogorov-Smirnov test for distribution equivalence. We expect image texture classification methods to outperform particle size analysis for more challenging real-world powder classification tasks by capturing additional information about particle morphology and surface textures, which add complexity to the image segmentation task inherent in particle size distribution estimation.