Optical granulometry
Photographic measurement of the distribution of grain sizes in a material
From Wikipedia, the free encyclopedia
Optical granulometry is the process of measuring the different grain sizes in a granular material,[1] based on a photograph. Technology has been created to analyze a photograph and create statistics based on what the picture portrays. This information is vital in maintaining machinery in various trades worldwide. Mining companies can use optical granulometry to analyze inactive or moving rock to quantify the size of these fragments.[2] Forestry companies can zero in on wood chip sizes without stopping the production process, and minimize sizing errors.[3]
| Granulometry | |
|---|---|
| |
| Basic concepts | |
| Particle size, Grain size, Size distribution, Morphology | |
| Methods and techniques | |
| Mesh scale, Optical granulometry, Sieve analysis, Soil gradation | |
Related concepts | |
| Granulation, Granular material, Mineral dust, Pattern recognition, Dynamic light scattering | |
With more photoanalysis technologies being produced, mining companies have shown an increased interest in these types of systems because of their ability to maintain efficiency throughout the mining process.[4] Companies are saving millions of dollars annually because of this new technology, and are cutting back on maintenance costs on equipment.[citation needed]
In order for optical granulometry to be completely successful, an accurate photo must be taken – under sufficient lighting, and using proper[clarification needed] technology – to obtain quantified results.[5] If these requirements are met, an image analysis system can be implemented.
The process
Software uses four basic steps in determining the average size of material:
See the Wikipedia article on Photoanalysis to see how mining, forestry and agricultural companies are using this technology to improve quality control techniques.
Smartphone-based, segmentation-free estimation of grain size distribution
Recently, a methodology has emerged by which soil grain size distribution can be inferred from optical images acquired with commodity smartphones by training convolutional neural networks to predict parameters of the distribution curve directly from the image, without explicit image segmentation [6]. In this approach, a standardized image of a soil surface is captured under controlled conditions, preprocessed to reduce device-specific variability, and passed to a regression model that outputs the parameters of a cumulative distribution function e.g., a two-parameter Weibull curve. The resulting distribution can be used to derive geotechnical descriptors and class boundaries.