— At the same time, disparities in many image properties exist among different rock mass surface images due to large varieties of the intensive information on rock mass surfaces at different outcrops, which is the main reason for no unified process for the image pre-processing in rock fracture skeleton extraction now.
— For an overview of micro-CT image acquisition, the issues associated with image processing, ... However, to the best of the authors' knowledge, the U-Net architecture has not yet been employed for the SR processing of micro-CT images of real rocks. The current paper demonstrates that the U-Net architecture can successfully be used for …
Smal et al. [3] proposed a novel algorithm that allows localization and quantification of sub-resolution porosity. Accurate segmentation of various digital rock images, like CT scans, …
— This paper presents an approach for capturing rock heterogeneity that combines peridynamic theory, digital image processing (DIP), and low-field nuclear magnetic resonance (NMR) imaging. By processing the magnetic resonance images (MRIs) of the rock material, the microstructure distribution is obtained, and the attenuation …
— Through the processing of Rolling Ball, the raw image of rock fragments with a poor illumination and shadow effect was converted and then sliced into an image as shown in Fig. 13 (b), and then the color gradient distribution was also acquired, in which the boundary features of rock fragments were further enhanced based on the de-background ...
— 2.2.1 Image Processing Pipeline 1: Non-local Means Filtering and Watershed Segmentation. The first image processing pipeline used makes use of a filter and segmentation combination that has been widely used in studies of porous rocks. Filtering options typically applied in imaging permeable media are reviewed in Kaestner et al. . …
— Digital Rock Images have been widely used for rock core analysis in petroleum industry. And it has been noticed that the resolution of Digital Rock Images are not fine enough for complex real-world problems. ... -level residual up-projection activation network for image super-resolution. in 2019 IEEE International Conference on Image …
— Rock image classification is a fundamental and crucial task in the creation of geological surveys. Traditional rock image classification methods mainly rely on manual operation, resulting in high costs and unstable accuracy. While existing methods based on deep learning models have overcome the limitations of traditional methods and achieved …
— The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters …
Rock fracture tracing is very important in many rock-engineering applications. This paper presents a new methodology for rock fracture detection, description and classification based on image processing technique and support vector machine (SVM). The developed algorithm uses a number of rock surface images those were taken by sophisticated CCD …
— Rock mass structural data analysis using image processing techniques (Case study: Choghart iron ore mine northern slopes) M. Mohebbi *, A.R. Yarahmadi Bafghi, M. Fatehi Marj i and J. Gholamnejad
— Image enhancement of rock piles plays an important role in rock size distribution. To measure the rock size, the original images are not of good quality. So our method is used to enhance the image ...
— Image processing and analysis techniques are commonly utilized in various fields such as geology, underwater engineering, environmental conservation, marine resource exploration, and soil and geological assessments, particularly for examining drilling rock samples. However, processing images of rocks drilled underwater is challenging …
Image segmentation is a crucial step in image analysis and computer vision, with the goal of dividing an image into semantically meaningful segments or regions. The process of image segmentation assigns a class label to each pixel in an image, effectively transforming an image from a 2D grid of pixels into a 2D grid of pixels with assigned ...
PDF | On Jun 1, 2022, Yanan Zhang and others published Peridynamic Simulation of Heterogeneous Rock Based on Digital Image Processing and Low-Field Nuclear Magnetic Resonance Imaging | Find, read ...
— Many techniques are available to extract information by computer manipulation of images and, therefore, this approach has great potential for the study of rock art. Processing procedures such as ...
— The features of the digital grey images of the rock thin sections are extracted using image processing technique in a neural network toolbox, and then the features are as input of a neural network ...
— The digital rock mass rating (DRMR) developed by Monte (2004) uses basic image processing procedures and calculations to estimate a classification rating from digital images of rock masses. The rating system incorporates fracture information collected from a discontinuity trace map (e.g. length, spacing, large-scale, roughness, rock bridge ...
— where, R represents the red color, G is the green color, B stands for the blue color, θ is the angle calculated toward the red axis in the color space of the HSI, H determines the image hue, S shows the saturation, and I represents the intensity of the image. 3.2.2 Second-order Grey Level Co-Occurrence Matrix. The statistical texture …
— In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation …
A complete algorithm for starting with a digital image of a rock face, automatically delineating traces, and extracting three dimensional joint or fracture orientation data is described. ... Less emphasis is placed on the pre-processing of the image, as different images require different levels of filtering, but nearly all pre-processing ...
— rock mass based on image processing., Journal of Rock Mechanics and Geotechnical Engineering (2017), doi: 10.1016/j.jrmge.2017.05.001. ... Images of rock masses around the work ing taken at (a ...
Experiments on rock CT and SEM images show that fine-tuning significantly enhances SAM's performance, enabling high-quality mask generation in digital rock image …
— In this study, we present applications of several convolutional neural networks (CNN) for rapid image denoising, deblurring and super-resolving digital rock …
— With the rapid development of computer technology, deep learning (LeCun et al., 2015) techniques are used in various areas such as image segmentation and classification, natural language processing, and target recognition, etc. Convolutional Neural Networks (CNNs) are the most representative deep learning algorithms for image …
— Data pre-processing of rock images: (A) Image slicing (B) Data augmentation. 4.1.2 Data augmentation. rAfter image slicing, the training dataset is expanded to 27,324 images. The dataset used consisted of a relatively small number of images for training network. The data augmentation used in this study to expand the …
— A digital image processing method was proposed to evaluate and analyze features of fractures and the size of fragments with different complexities, as shown in Fig. 1. ... A semi-automated methodology for discontinuity trace detection in digital images of rock mass exposures. Int. J. Rock Mech. Min. Sci. (2000) Z. Pi et al. Digital image ...
— The pre-processing process of blasted rock image and its influence on the segmentation results are described, and the Phansalkar method is introduced.
