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, …
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— 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.
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— 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].
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— Therefore, studying the microstructure of rocks through image processing technology has become a new research trend [8, 9]. In recent years, with the rapid development of image processing technology, quantitative analysis of rock microstructures using high-resolution images has become feasible [10].
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— 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 …
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— In this study, we present applications of several convolutional neural networks (CNN) for rapid image denoising, deblurring and super-resolving digital rock …
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In addition, limitations in existing stable diffusion models are also discussed, including the lack of real digital rock images, and not being able to fully understand the mechanisms behind physical processes. Therefore, it is suggested to develop new models tailored to digital rock images for further progress. ... IEEE Transactions on Image ...
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— 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 ...
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— 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 …
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— 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 ...
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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 ...
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— 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 …
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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
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— Features such as rock texture and mineralogy have a degree of control on how the rock behaves in the processing plant and thus can affect the project's economic feasibility. ... Image processing and data extraction from X-ray Tomography images. The image processing stage of the 3D tomograms from the X-ray microtomography …
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— This study employed the image processing technique to examine the ditch cuttings and to identify the wellbore failure types. This method utilizes the quantitative data derived from size and shape analyses of particles from image processing. A new criterion for the classification of cavings was suggested here according to Fig. 11. It is based on ...
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— The pre-processing process of blasted rock image and its influence on the segmentation results are described, and the Phansalkar method is introduced.
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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 …
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— 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 …
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— In this paper, we propose an approach to enhancing identification of digital rock images with super-resolution neural network (SSRN) which enables low-resolution …
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— 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
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— 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 …
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— 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 …
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— 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 ...
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— 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. . …
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— In this paper, we present ground-truthing of digital rock images using texture analysis. We propose a deep learning–based approach for automated segmentation …
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— 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 ...
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Processing currently works with GIF, JPEG, and PNG images. The img parameter specifies the image to display and by default the a and b parameters define the location of its upper-left corner. The image is displayed at its original size unless the c …
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— 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.
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— 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.
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— 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 …
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