Characterization of Particle Assemblies Through Digital Image Processing (original) (raw)
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An Image-Based Method to Determine the Particle Size Distribution (PSD) of Fine-Grained Soil
Rudarsko-geološko-naftni zbornik
This paper discusses the PSD eff ects on the behavior of soil and its properties. The coarse grain part of soil can be analyzed with sieve analysis. Evaluating the PSD of fi ne-grained soil with the traditional method (hydrometer analysis) is a time-consuming process, has laboratory complexities and the obtained results are not accurate. Due to these limitations, there is a demand for a method that can off er faster data with more accuracy for geotechnical engineering. In this paper, the digital image processing (DIP) method is proposed using microscopic images and MATLAB software to determine the PSD of fi ne-grained soil. The DIP method was compared with the hydrometer analysis. The analysis of the data obtained from these two methods indicated that the image based method is capable of providing faster and more comprehensive results of the PSD.
Sustainable Soil Particle Size Characterization Through Image Analysis
Geotechnical and Geological Engineering, 2013
A rapid, clean, low-energy, image-based method for determining the grain size distribution of soils by image analysis has been developed. The method is called Sediment Imaging or ''Sedimaging''. It develops the grain size distribution for particles in the range between a U.S. Standard Sieve No. 10 (2.0 mm openings) and U.S. Standard Sieve No. 200 (0.075 mm openings) range. The system utilizes a high resolution Nikon D7000 digital single lens reflex camera and image processing software developed specifically for interpreting the images and producing the resulting grain size distribution. The Sedimaging system is more sustainable and environmentally friendly than traditional sieving by virtue of its far lower power needs, less water consumption, longer equipment life and less maintenance. From the environmental and health perspectives, Sedimaging is less noisy, generates no vibrations and produces no airborne particulates. Sedimaging is also significantly faster than sieving and produces thousands of data points compared to typically 8 by sieving; it also automatically computes grain size distribution metrics such as the coefficients of uniformity and gradation.
Effect of particle size and shape on the grainsize distribution using Image analysis
International Journal of Civil and Structural Engineering, 2011
Grain-size distribution that is one of the base properties of soils and soil classification systems gives idea about engineering properties of soils. Determination of grain-size distribution derived from mechanical method (sieving) is time consuming and difficult. Hence, the image analysis methods for determination of grain-size distribution have been also investigated by several researchers. In that research, the grain-size distributions for ten soil samples with different sizes and shapes are determined with image analysis methods. The effects of particle shape (i.e., elongated, flat, spherical) and volume computation techniques (cube, cylinder, ellipsoid, and modified ellipsoid) on grain-size distribution were also researched. The results of the research indicated that the shape of particles significantly affected grain-size distribution. Additionally, modified ellipsoid method is determined as the best volume computation method.
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Computers and Geotechnics, 2016
Image-based soil particle size and shape characterization relies on computer methods to process and analyze the images. For contacting particles spread on a flat surface this requires delineation of particle boundaries through shape-based image segmentation. The traditional method using watershed analysis fails for particles that have constrictions (are peanut-shaped). The oversegmentation interprets such particles as being two, thereby underestimating the long particle dimension by about 50% and overestimating particle sphericity by about a factor of two. This paper presents a solution to the problem of oversegmentation through morphologic reconstruction. The key to this improvement is distinguishing the necks in peanut shaped particles from actual contacts between particles. A parameter a is defined to quantify the necks and contacts. Approximately 220,000 particles in a range of 2.0-35.0 mm having various shapes and angularities were studied to find typical a values for necks and contacts. An algorithm is proposed to correct the oversegmentation based on a. The results show that this improved watershed analysis accurately segments sand particles at contacts while preserving the continuity of peanut shaped particles. Example lab tests demonstrate the significance of the problem and its solution.
Three-Dimensional Digital Image Processing And Reconstruction Of Granular Particles
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MRS Proceedings, 1994
Preliminary results of a research aimed to identify the influence of the morphological characteristics of the grains constituting a quartz sand on the mechanical behaviour of the sand itself are presented. The tests were conducted on different size class samples resulting from a grain-size classification of the sand following the international standard classification (ASCII) procedure. For each sample traditional direct shear laboratory tests and an accurate characterisation of morphological and morphometrical properties of their constituting elements (grains) by means of digital image processing techniques were conducted. The statistical distribution of the different parameters derived following the two above mentioned procedures was studied and the results discussed.
Minimum image quality for reliable optical characterizations of soil particle shapes
Computers and Geotechnics
Remarkable advances have been seen in image-based methods for automating soil particle shape characterizations in the last decade. However, the accuracy and reliability of image-based methods has rarely been questioned. This study shows that image quality affects the computational results of particle shape descriptors, including aspect ratio, sphericity, convexity, circularity, and roundness. These descriptors display a hierarchy of resistance to the effects of low image quality. The particle length, perimeter, and area are used as controlling parameters for quantifying the influence of image quality. The minimum requirements for ensuring reliable image-based shape characterization of these parameters are established.
Digital Image Analysis for the Determination of Size and Shape Parameters of Sand Grains
For ages, sieve analysis test remained as a convenient method to determine particle size distribution of granular materials. In sieve analysis, the particle size is characterized by a single linear dimension representing the minimum square sieve aperture through which the particle will just pass. This one dimension size description of sand grains is not sufficient to characterize the sand and to understand the engineering behaviour of the sand. Therefore, it is necessary to adopt a new technique which can give more insight to proper characterization of the sand grains. Digital Image Processing gives more reliable results because it not only generates the dimensional size of the grains, as in case of mechanical sieving, but also the shape parameters of soil grains oriented on its largest base area. Thus it can be used for more accurate determination of the size distribution of sand grains. In this study, a monochromatic light and a high definition camera were used to generate images ...
Automated separation of touching grains in digital images of thin sections
Computers & Geosciences, 2002
The determination of textural properties of granular material with image analysis is generally troubled by the fact that touching grain sections merge into single features. Without separation of these touching grain sections, the textural properties derived from the images contain substantial bias. Existing methods for separating touching grains, like erosion-dilation cycles or watershed segmentation, are time-consuming and/or alter the textural properties of the grain sections analyzed. An alternative computer algorithm is presented to separate touching grain sections in binary images of granular material. The algorithm detects characteristic sharp contact wedges in the outline of touching grain sections and creates an intersection after checking if the angle of the contact wedge is smaller than a user-defined threshold value. The performance of the new algorithm is compared to that of the watershed segmentation method. The resulting grain-size distributions after applying automated separation techniques, were verified with the size distribution obtained with a laboratory laser particle sizer. The algorithm shows improved preservation of size and shape characteristics of the granular material over the watershed segmentation method. #
Soil physical properties are used for soil classification and hence serve as the first estimator of almost every mechanical and hydrologic soil behavior, but the usual laboratory testing method of estimating these physical properties is time consuming, burdensome, and somehow subjective, hence posing a challenge to geotechnical engineers and soil lab technicians. To minimize these shortfalls to an acceptable level and make the determination of soil physical properties more objective, this study utilized digital image processing and analysis (DIP&A) approach to establish relationships between the physical properties of soil (water content, density-wet bulk and dry, porosity, and void ratio) and the image feature of its fragmented aggregates identified as shape factors and recognized as aspect ratio and roundness in the study. Shape factors are used to describe the overall geometric characteristics of a particle. In this study, samples of soil were collected and laboratory test performed as per the American Society of Testing and Materials (ASTM) Standards to determine the physical properties and the same samples fragmented into appropriate sizes and their images acquired for image processing and analysis to determine the image feature. The physical properties of soil were statistically correlated and regressed against the image feature using appropriate regression model with the correlation coefficient (r) as the basis of the correlation. The r value varies from 0.5 to 0.6. The outputs of the regression analysis were curvilinear models that represent the relationship between soil physical properties and shape factors. The results of the digital image analysis were verified by conducting laboratory test and image analysis of soil sample collected from different site other than the one previously used to develop the relation models and the new values of the validated sample replaced in the developed models, resulting in a reasonable average percent error that varies from 1.05% to 3.57% between conventional laboratory testing and the proposed new method-DIP&A. The results indicate that DIP&A method proposed in the study appears to be a useful and promising method for estimating the physical properties of soil and was also shown to be a valuable tool for quantifying the geometric properties of soil aggregates.