Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS

Moreno, Gamaliel; Sarocchi, Damiano; Santana, E. Arce; Borselli, L.

doi:10.1016/j.cageo.2015.09.007

Cited by 13 publications

(1 citation statement)

References 33 publications

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“…Digital image analysis techniques have been used in the literature to determine the particle size: Chávez et al (2015) developed a method to find the granulometry in sedimentary deposits based on color image segmentation; Sarocchi, Borselli, and Macías (2005) used image methods to find granulometric profiles of pyroclastic deposits; Butler, Lane, and Chandler (2001), Graham, Reid, and Rice (2005), and Buscombe, Rubin, and Warrick (2010) used methods related to digital image analysis to find the granulometry of fluvial sediments on a surface. The complete granulometric distribution of a particle group can be found by image processing, as Raschke and Hryciw have shown through a semiautomatic method for granular soils with particles between 0.023 and 5.7 mm (Raschke and Hryciw 1997).…”

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confidence: 99%

Evaluation of a Simple and Affordable Image-Based Procedure to Measure Particle Size Distribution

Nostas

Mas

2021

Geotechnical Testing Journal

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The classical measurement method of particle size for sands and gravels is sieving, but in recent years, imaging methods have been shown to be more reliable. However, imaging procedures require specialized devices (such as Camsizer), proprietary software (such as Optgran-CS), dedicated illumination, or other issues. Furthermore, image resolution in digital cameras improves constantly over time, including in cameras in cellular phones, which leads to the idea that using a standard commercial device and a simple procedure could give results that could be accurate enough. In this work, we first define a simple way of finding the particle size distribution without using any special device, only with a commercial camera and a wellknown free image-processing software. Once defined, we analyze the error magnitude and dispersion of the method with different samples and light intensities. To make the procedure even more accessible, the use of a smartphone camera is also studied. The results show that the method can be used taking into account a correction factor within a narrow interval, which makes this simple method also robust.

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