— 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.

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 …

 — 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 ...

 — 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 …

 — 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. . …

 — Singh et al. (2010) proposed an approach for identifying textural features via the image processing of thin section images from different basalt rock samples. They extracted 27 parameters from 300 thin section images, built a multilayer perceptual neural network model, and used these parameters to train the model. ... Textural identification …

 — 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 …

 — 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 …

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 ...

 — 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 ...

 — 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 …

 — 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 ...

 — The pre-processing process of blasted rock image and its influence on the segmentation results are described, and the Phansalkar method is introduced.

 — 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 ...

 — 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 ...

 — 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 …

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

 — 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 …

 — 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

 — In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation …

 — Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative …

 — 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 …

 — Textural identification of basaltic rock mass using image processing and neural network. Computational Geosciences, 14(2), 301–310. Article Google Scholar Singh, V., & Rao, S. M. (2005). Application of image processing and radial basis neural network techniques for ore sorting and ore classification.

 — 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].

 — Through multiple image processing, dark region contrast of rock surface images is enhanced while noise is reduced. After this process, the effect of Frangi filtering can be significantly improved.

 — 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

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 …

 — 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 …

 — 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 …