Pixel-Vernier: A General Image-Based Approach For Particle Size Distribution Estimation

Abstract

Particle size distribution estimation (PSDE) is a fundamental task for heterogeneous materials characterization and modeling. This paper presents a general image-based approach for PSDE, which is called collectively “Pixel-Vernier” or PV for short. Meaningful image segmentation is the main problem to be solved for image-based PSDE. To this end, the proposed approach combines markers-controlled watershed segmentation with a clustering algorithm to solve the delineation of the boundaries of the particles. The combined approach is embedded in a coarse-to-fine strategy using a one training parameter to adapt the algorithm to the underlying distributions of the particle size. This training parameter is restricted to the size of an averaging filter. PV decomposes the image into separate particle regions. The final results of these regions are used to compute several geometric attributes for particles such as the semi-major axis, the semi-minor axis, and the equivalent diameter. Then, the geometric attributes of all the particles are used to estimate size distribution and relevant statistics. PV can be used in a laboratory as well as in a field setting. It is tested successfully on a diverse set of images that represent materials like soils, texture, rocks, and Mars surface geology.