Image based shape characterization of granular materials and its effect on kinematics of particle motion

Quantification of particle shape features to characterize granular materials remains an open problem till date, owing to the complexity involved in obtaining the geometrical parameters necessary to adequately compute the shape components (sphericity, roundness and roughness). A new computational method based on image analysis and filter techniques is proposed in this paper to overcome this difficulty. In this method, operations are performed on binary images of particles obtained from raster images (collection of pixels) by the process of image segmentation. The boundary of particles captured in 2D images consist of micro, meso and macro scale features on which filter techniques are applied to remove the micro level features for the quantification of particle roughness and to obtain a roughness free boundary. A robust algorithm is then written and implemented in MATLAB to obtain the complete geometry of the particle boundary (free from roughness features) and to identify the precise corner and non-corner regions along the boundary. This information is used to quantify the roundness (as per Wadell in J Geol 40:443-451, 1932) and sphericity of particles. The proposed methodology to measure roundness and sphericity is compared against standard visual charts provided by earlier researchers. Finally, the methodology is demonstrated on real soil particles falling across a wide range of sizes, shapes and mineralogical compositions. Also, an idea to comprehend the kinematics of particle motion based on its concavo-convex features is discussed with two proposed novel descriptors and a visual classification chart