Assistant professor, Lehigh University, Bethlehem, PA
Associate professor, University of Michigan, Ann Arbor, MI
A semi-automated technique for obtaining the grain-size distribution (GSD) of granular soils using computer vision is presented. Backlighted digital images of a soil specimen dispersed over a glass specimen plate are acquired at three different magnifications. Images of the specimen are acquired by placing the specimen plate randomly beneath the field of view of a charged-coupled device (CCD) video camera. The size of particles with projected areas from 50 to 2000 pix2 is measured in each image. Multiple images are acquired at each magnification until the measured size distribution of particles counted at that magnification stabilizes. Probabilistic corrections are then used to obtain a statistically unbiased GSD from the image data obtained at all three magnifications. A comparison of GSD data for two uniform and two nonuniform soils using both computer vision and sieving is presented.
Paper ID: GTJ10410J