Processing of micro-CT images of granodiorite rock …
— 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 …
Zero-Shot Digital Rock Image Segmentation with …
— Meta AI's Segment Anything Model (SAM) revolutionized image segmentation in 2023, offering interactive and automated segmentation with zero-shot capabilities, essential for digital rock …
Indirect tensile test modelling for inhomogeneous rock …
— After comparing with the original image of the rock sample in Fig. 1, one can observe that the individual minerals have been automatically detected correctly and successfully.The above digital image processing techniques can produce good-quality microstructures for scanned images of granite cross-sections.
Applications of Physically Accurate Deep Learning …
— image denoising process applied to a micro-CT rock image. The denoised image can clearly capture the edge sharpness between each phase, whereas the noisy image struggles to separate
Determination of geological strength index of jointed rock …
— 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 ...
Frontiers | Rock image classification using deep residual …
— 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 …
(PDF) Rock mass structural data analysis using …
— 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
Rock images classification by using deep …
— 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 ...
Rock fracture tracing based on image processing and SVM
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 …
A method of blasted rock image segmentation based on …
— The pre-processing process of blasted rock image and its influence on the segmentation results are described, and the Phansalkar method is introduced.
FMI image based rock structure classification using classifier
— Formation Micro Imager (FMI) can directly reflect changes of wall stratums and rock structures, and is an important factor to classify stratums and identify lithology for the oil and gas exploration. Conventionally, people analyze FMI images mainly with manual processing, which is, however, extremely inefficient and incurs a heavy workload for …
Research on Fragmentation Distribution of Rock Based on Digital Image
— Based on this, this paper first studies the theory and method of rock mechanics, then analyses the application of digital image processing technology, and finally gives the specific research on ...
Automatic detection and characterization of discontinuity …
— 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 ...
DIGITAL IMAGE PROCESSING LECTURE NOTES
Color image processing: It is an area that is been gaining importance because of the use of digital images over the internet. Color image processing deals with basically color models and their implementation in image processing applications. Wavelets and Multiresolution Processing : These are the foundation for representing image
Zero-Shot Digital Rock Image Segmentation with a Fine …
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 …
Segmentation of digital rock images using texture analysis …
— Image segmentation is an important part of the standard digital rock physics (DRP) workflows. In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation which is validated using the extracted ground-truth. To generate the ground-truth, we …
Image processing in Python using Pillow
— Properties of the Image object. There are several properties of the image we can access to get more data from the image: image.width returns the width of the image; image.height returns the height of the image; image.format returns the file format of the image (e.g., .JPEG, .BMP, .PNG, etc.); image.size returns the tuple height and weight of …
satellite-image-deep-learning/techniques
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 ...
Multi-channel attention transformer for rock thin-section image …
— In terms of pore extraction, image processing technology has been employed to extract the edges of pores, ... We used an internally provided rock thin section segmentation dataset with 20 samples, each sample has seven images. Each rock particle image resolution ranges from 2000*1000–7000*4500 with three channels of rgb. The …
Digital Rock Image Enhancement via a Deep Learning …
— 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 …
Peridynamic Simulation of Heterogeneous Rock Based on Digital Image
— 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 …
The Sensitivity of Estimates of Multiphase Fluid and Solid …
— 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. . …
Enhancing identification of digital rock images using super …
— 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 …
Determination of geological strength index of jointed rock …
— 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 ...
Research on a High-Performance Rock Image Classification …
— Efficient and convenient rock image classification methods are important for geological research. They help in identifying and categorizing rocks based on their physical and chemical properties, which can provide insights into their geological history, origin, and potential uses in various applications. The classification and identification of rocks often …
Processing of rock core microtomography images: Using …
— 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 …
A semi-automated methodology for discontinuity trace detection …
— General-purpose digital image processing techniques (e.g. greyscale threshold, greyscale smoothing and edge detection) have been used previously to highlight discontinuity traces in digital images of rock mass exposures, and so assist in the analysis of discontinuity geometry [3], [4].
Automatic detection and characterization of discontinuity …
— 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 ...
Reconstructing high fidelity digital rock images using deep
— In this study, we present applications of several convolutional neural networks (CNN) for rapid image denoising, deblurring and super-resolving digital rock …
Enhancing Rock Image Segmentation in Digital Rock …
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, …
Segmentation of digital rock images using 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 which is …
Automatic identification of minerals in thin sections using image …
— 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 …