Minimum image quality for reliable optical characterizations of soil particle shapes (original) (raw)
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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.
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.
Particle shape characterisation using Fourier descriptor analysis
Geotechnique, 2001
A novel technique for the objective assessment of particle shape is presented. The tech~que uses complex Fourier shape descriptors and image analysis of SEM photographs of sand grain~ to provide an accurate quantification of particle morphology and texture. Three lower order Fourier descriptors, denoted "Signature Descriptors" provide measures of Elongation, Triangularity and Squareness, whilst an additional descriptor, denoted "Asymmetry" provides a measure of particle irregularity. They describe the overall shape of soil particles (defined as "morphology"). A summary of higher order descriptors provides textural information which is related to local roughness features (defined as "texture"). The results of studies on three silica sands (two standard, laboratory-use and one natural, unprocessed) and one carbonate sand are presented. Breakage of particles by crushing is shown to affect the morphological signature differently depending on the type of sand, though it does not significantly alter texture. The study highlights the importance of microscopy in revealing sand grain shape and texture and shows that simple statistical tools may be used to translate the information provided by relatively few grains to that of a larger body of soil.
Characterization of Particle Assemblies Through Digital Image Processing
Imaging techniques are proving to be a viable alternative to mechanical sieving for determination of soil grain size distribution. While such distributions are relatively easily obtained when the soil grains are non-contacting, interpretation of in-situ images of contacting grains (assemblies) is considerably more difficult. As such, two approaches were developed for interpreting such images including: edge detection and completion by Hough transforms with active contouring; and pixel density analysis. A reasonably precise measure of grain size can be obtained using circular Hough transforms in conjunction with active contouring. However, the method is computationally very intensive and has only been tested on highly idealized assemblies of soil grains. Edge pixel densities (EPD) and their coefficients of variation with increasing sampling window size provide a rapid means for assessing grain sire and uniformity and for detection of soil interfaces within an image.
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.
Granular Matter, 2017
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. Keywords Shape features • Image analysis • Kinematics • Corner and non-corner regions • Gaussian regression filter • Granular material
Image Analysis of Soil Micromorphology: Feature Extraction, Segmentation, and Quality Inference
EURASIP Journal on Advances in Signal Processing, 2004
In this paper we present an automated system that we have developed for estimation of the bioecological quality of soils using various image analysis methodologies. Its goal is to analyze soilsection images, extract features related to their micromorphology and relate the visual features to various degrees of soil fertility inferred from biochemical characteristics of the soil. The image methodologies used range from low-level image processing tasks such as nonlinear enhancement, multiscale analysis, geometric feature detection, size distributions to object-oriented analysis such as segmentation, region texture and shape analysis. *
Soil Textural Classification by a Photosedimentation Method
Applied Optics, 1998
A photosedimentation technique is used to analyze the size composition of soil samples. The number and size of the particles are determined, respectively, by the Stokes formula and the Beer-Lambert law, measuring time-of-flight and laser light attenuation simultaneously and hence evaluating solution turbidity. A simple software procedure has been developed to obtain fractional volume size distribution, taking into account the particle's optical properties depending mainly on its size and refractive index. Laboratory measurements on calibrated particulates, showing their reproducibility and validation as well as a classification of ground samples, are presented. Size distribution data can then be utilized to obtain a textural classification of the soil samples for agricultural applications.
Segmentation of contacting soil particles in images by modified watershed analysis
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
This thesis presents a method for digitization of the two-dimensional shape of granular particles by means of photo microscopy and image processing techniques implemented using a software package from Media Cybernetics, Inc: Image-Pro Plus 5.1 and the add-ins Scope-Pro 5.0, SharpStack 5.0 and 3D Constructor 5.0. With the use of these tools, it was possible to implement an efficient semi-automated routine that allows the digitization of large numbers of two-dimensional silhouettes of particles in minimum time, without endangering the quality and reliability of the shapes obtained. Different sample preparation techniques, illumination systems, deconvolution algorithms, mathematical functions, filtering techniques and programming commands are brought into play in order to transform the shape of the two-dimensional projection of particles (captured as a set of successive images acquired at different planes of focus) into a binary format (black and white). At the same time, measurements and statistical information such as grain size distribution can be analyzed from the shapes obtained for a particular granular soil. This information also includes but it is not limited to perimeter, area, diameter (minimum, maximum and mean), caliper (longest, smallest and mean),roundness, aspect ratio and fractal dimension. Results are presented for several sands collected from different places around the world. In addition, some alternatives for threedimensional shape reconstruction such as X-ray nano tomography and serial sectioning are discussed.