Gotowe bibliografie tematyczne / Segmentation des pores

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  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji
  5. Raporty organizacyjne

Gotowa bibliografia na temat „Segmentation des pores”

Autor: Grafiati
Data publikacji: 7 września 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Segmentation des pores"

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Sintorn, Ida-Maria, Stina Svensson, Maria Axelsson, and Gunilla Borgefors. "Segmentation of individual pores in 3D paper images." Nordic Pulp & Paper Research Journal 20, no. 3 (2005): 316–19. http://dx.doi.org/10.3183/npprj-2005-20-03-p316-319.

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Bauer, Benjamin, Xiaohao Cai, Stephan Peth, Katja Schladitz, and Gabriele Steidl. "Variational-based segmentation of bio-pores in tomographic images." Computers & Geosciences 98 (January 2017): 1–8. http://dx.doi.org/10.1016/j.cageo.2016.09.013.

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Liu, Lei, Qiaoling Han, Yue Zhao, and Yandong Zhao. "A Novel Method Combining U-Net with LSTM for Three-Dimensional Soil Pore Segmentation Based on Computed Tomography Images." Applied Sciences 14, no. 8 (2024): 3352. http://dx.doi.org/10.3390/app14083352.

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The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT images frequently harbors inaccuracies. This paper introduces a novel three-dimensional pore segmentation method, BDULSTM, which integrates U-Net with convolutional long short-term memory (CLSTM) networks to harness sequence data from CT images and enhance the precision of pore
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Yosifov, Miroslav, Patrick Weinberger, Bernhard Plank, et al. "Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network." Acta Polytechnica CTU Proceedings 42 (October 12, 2023): 87–93. http://dx.doi.org/10.14311/app.2023.42.0087.

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This study demonstrates the utilization of deep learning techniques for binary semantic segmentation of pores in carbon fiber reinforced polymers (CFRP) using X-ray computed tomography (XCT) datasets. The proposed workflow is designed to generate efficient segmentation models with reasonable execution time, applicable even for users using consumer-grade GPU systems. First, U-Net, a convolutional neural network, is modified to handle the segmentation of XCT datasets. In the second step, suitable hyperparameters are determined through a parameter analysis (hyperparameter tuning), and the paramet
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Tomažinčič, Dejan, Žiga Virk, Peter Marijan Kink, Gregor Jerše, and Jernej Klemenc. "Predicting the Fatigue Life of an AlSi9Cu3 Porous Alloy Using a Vector-Segmentation Technique for a Geometric Parameterisation of the Macro Pores." Metals 11, no. 1 (2020): 72. http://dx.doi.org/10.3390/met11010072.

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Most of the published research work related to the fatigue life of porous, high-pressure, die-cast structures is limited to a consideration of individual isolated pores. The focus of this article is on calculating the fatigue life of high-pressure, die-cast, AlSi9Cu3 parts with many clustered macro pores. The core of the presented methodology is a geometric parameterisation of the pores using a vector-segmentation technique. The input for the vector segmentation is a μ-CT scan of the porous material. After the pores are localised, they are parameterised as 3D ellipsoids with the corresponding
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Ng, Hwee Ping, Qijian Chan, Zheng Jie Tan, Ronnie Ssebaggala, and Joseph John Lifton. "Segmenting spatter particles on additively manufactured surfaces using deep learning." Surface Topography: Metrology and Properties 13, no. 1 (2025): 015006. https://doi.org/10.1088/2051-672x/ada6e1.

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Abstract Metal additively manufactured (AM) surfaces do not exhibit the same surface features as machined surfaces. Rather than cutting marks, the additive surface may display surface features such as spatter particles, weld tracks, cracks, and surface breaking pores. These features are not well described by surface height parameters that were developed for machined surfaces. Therefore, an AM specific surface characterisation approach is required; feature based surface characterisation is a promising approach, but it requires surface features to be manually segmented which is a subjective proc
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Tomina, E., K. Zhuzhukin, A. Dmitrenkov, et al. "Study of the quality of impregnation and structural features of birch wood using the method of micro-X-ray computed tomography." Forestry Engineering Journal 14, no. 4 (2025): 172–86. https://doi.org/10.34220/issn.2222-7962/2024.4/12.

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Quality control of the internal structure of wood is an urgent task from both scientific and practical points of view. Existing control methods mainly imply a destructive approach associated with the destruction of a part of the product, which in some cases is impossible. In this study, the morphological features of the void space of wood were studied with an assessment of open porosity, pore size distribution, as well as a separate analysis of the void space with spatial determination and assessment of the sizes of pores filled with impregnation and empty pores using the method of micro-X-ray
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Tong, Tong, Yan Cai, Da Wei Sun, and Peng Liu. "Automatic Segmentation of Pores in Weld Images Based on Transition Region Extraction." Applied Mechanics and Materials 217-219 (November 2012): 1964–67. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.1964.

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In allusion to the complex images of weld defects, weak contrast between the target and the background, a new segmentation method based on gray level difference transition region extraction is proposed. The paper analyzes the characteristic of weld defects, and then low-pass filtering and contrast enhanced are used to enhance the clarity. Finally, we extract the transition region and confirm a threshold for defects segmentation. The experimental results show that the method can extract the transition region more accurate, and segment the image much better in complex environment.
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Zhao, Keying, and Zhanghua Zhang. "NMR and SEM fractal dimensions explore shale pore structure taking the Upper Paleozoic shale in Ordos Basin as an example." PLOS One 20, no. 5 (2025): e0323968. https://doi.org/10.1371/journal.pone.0323968.

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In this paper, the fractal dimension is calculated by extracting pore parameters from SEM images and NMR experimental data, the pore structure heterogeneity in plane and space is comprehensively discussed, and the relationship between the fractal dimension and shale composition and physical parameters is discussed, providing new ideas for the study of shale reservoirs heterogeneity. Fractal dimension analysis of SEM images reveals that the shale pores of the Shanxi Formation can be divided into organic pores, inter-granular pores and micro-fractures. The average diameter of nano-scale pores is
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Yoon, Huisu, Semin Kim, Jongha Lee, and Sangwook Yoo. "Deep-Learning-Based Morphological Feature Segmentation for Facial Skin Image Analysis." Diagnostics 13, no. 11 (2023): 1894. http://dx.doi.org/10.3390/diagnostics13111894.

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Facial skin analysis has attracted considerable attention in the skin health domain. The results of facial skin analysis can be used to provide skin care and cosmetic recommendations in aesthetic dermatology. Because of the existence of several skin features, grouping similar features and processing them together can improve skin analysis. In this study, a deep-learning-based method of simultaneous segmentation of wrinkles and pores is proposed. Unlike color-based skin analysis, this method is based on the analysis of the morphological structures of the skin. Although multiclass segmentation i
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Rozprawy doktorskie na temat "Segmentation des pores"

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DELERUE, JEAN FRANCOIS. "Segmentation 3d, application a l'extraction de reseaux de pores et a la caracterisation hydrodynamique des sols." Paris 11, 2001. http://www.theses.fr/2001PA112141.

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Le sol et les materiaux poreux en general, peuvent etre vus comme l'union de deux parties : la partie solide, constituee de differents materiaux (argile, roche etc. ) et la partie vide (espace poral) par ou peuvent s'ecouler des fluides. Une connaissance precise de la structure 3d de la partie vide devrait permettre une meilleure comprehension des phenomenes d'ecoulement, voire meme une prevision des proprietes hydriques de ces materiaux. Les recents progres dans les domaines de l'acquisition d'image rendent de plus en plus abordable l'obtention d'images volumiques de sol, notamment grace a la
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Ding, Nan. "3D Modeling of the Lamina Cribrosa in OCT Data." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS148.

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La lame criblée (LC), située dans la tête du nerf optique, joue un rôle crucial dans le diagnostic et l'étude du glaucome, la deuxième cause de cécité. Il s'agit d'un maillage collagénique 3D formé de pores, par lesquels les fibres nerveuses passent pour atteindre le cerveau. L'observation 3D in vivo des pores de la LC est désormais possible grâce aux progrès de l'imagerie de tomographie de cohérence optique (OCT). Dans cette étude, nous visons à réaliser automatiquement la reconstruction 3D des pores à partir de volumes OCT, afin d'étudier le remodelage de la LC au cours du glaucome. La résol
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Sekkal, Rafiq. "Techniques visuelles pour la détection et le suivi d'objets 2D." Phd thesis, INSA de Rennes, 2014. http://tel.archives-ouvertes.fr/tel-00981107.

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De nos jours, le traitement et l'analyse d'images trouvent leur application dans de nombreux domaines. Dans le cas de la navigation d'un robot mobile (fauteuil roulant) en milieu intérieur, l'extraction de repères visuels et leur suivi constituent une étape importante pour la réalisation de tâches robotiques (localisation, planification, etc.). En particulier, afin de réaliser une tâche de franchissement de portes, il est indispensable de détecter et suivre automatiquement toutes les portes qui existent dans l'environnement. La détection des portes n'est pas une tâche facile : la variation de
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Części książek na temat "Segmentation des pores"

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Jiqun, Zhang, Hu Chungjin, Liu Xin, He Dongmei, and Li Hua. "An Algorithm for Rock Pore Image Segmentation." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46578-3_28.

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Kim, Hyunwook, Jinew Seo, Seiyoung Ryu, Joon hyung Park, Sungchul On, and Jinwha Choi. "Axis-Guided Quality Assessment and Multi-label Hippocampal and Ventricular Segmentation in Low-Resolution Pediatric Brain MRI." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83008-2_5.

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Abstract The Swoop system of Hyperfine Inc. is an affordable, ultra-low-field MRI developed for use in a clinical setting. However, despite its advantages, the relatively low resolution of 64mT MRI data poses additional challenges, especially in examining small structures such as the hippocampus or vessels. As a part of our attempt at the Low field pediatric brain magnetic resonance Image Segmentation and Quality Assurance (LISA) Challenge 2024, we developed two deep learning-based models. First, to evaluate the image quality of 64mT T2 brain MRI data, we implemented an axis classifier module
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Jiang, Hao. "Finding Human Poses in Videos Using Concurrent Matching and Segmentation." In Computer Vision – ACCV 2010. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19315-6_18.

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Krüger, Nina, Jan Brüning, Leonid Goubergrits, et al. "Deep Learning-Based Pulmonary Artery Surface Mesh Generation." In Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52448-6_14.

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AbstractProperties of the pulmonary artery play an essential role in the diagnosis and treatment planning of diseases such as pulmonary hypertension. Patient-specific simulation of hemodynamics can support the planning of interventions. However, the variable complex branching structure of the pulmonary artery poses a challenge for image-based generation of suitable geometries. State-of-the-art segmentation-based approaches require an interactive 3D surface reconstruction to prepare the simulation geometry. We propose a deep learning approach to generate a 3D surface mesh of the pulmonary arter
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Lu, Siwei, Xiaofang Zhao, Huazhu Liu, and Hongjie Liang. "Semiconductor Material Porosity Segmentation in Flame Retardant Materials SEM Images Using Data Augmentation and Transfer Learning." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240011.

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Non-halogenated flame retardants are becoming the trend in the development of polymer flame retardant materials due to their high flame retardant efficiency and low generation of toxic smoke gases. Non-halogenated flame retardants achieve flame retardancy by forming a dense char layer and generating non-combustible gases, with the micro-porous structure of the char residue being crucial for studying the flame retardant mechanism. This study focuses on the segmentation of pores in scanning electron microscopy (SEM) images of the combustion char layer of non-halogenated flame retardant materials
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Bhatti, Uzair Aslam, Muhammad Aamir, Zia Ur Rehman, et al. "Advancements and Emerging Trends in Deep Learning-Based Transformers for Medical Image Processing." In Advances in Medical Diagnosis, Treatment, and Care. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9816-6.ch004.

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The U-net Architecture is a U-shaped neural network developed from Fully Convolutional Networks FCNs in medical image segmentation. However, the traditional Convolutional Neural Networks (CNNs), like the one in U-Net, poses challenges in addressing long-distance relationships relating to the subject matter hence making their applicability to several segmentation tasks somewhat restrained. To counteract this, extending Transformer modules into U-shaped designs has received considerable interest, as an approach to boosting feature extraction and segmentation effectiveness. This paper also presen
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Nandhitha, N. M., S. Emalda Roslin, Rekha Chakravarthi, and M. S. Sangeetha. "Feasibility of Infrared Thermography for Health Monitoring of Archeological Structures." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210021.

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Archeological assets of the nation are to be preserved and rejuvenated. Ageing of these sites poses a major challenge in assessing the health of these structures. Hence it necessitates a technique that is non contact non invasive and non hazardous. Passive InfraRed Thermography is one such technique that uses an IR camera to capture the temperature variations. Thermal variations are mapped as thermographs. Interpretation of thermographs provides information about the health of the archeological structures. As the paradigm has shifted to computer aided interpretation, segmentation techniques an
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Hiremath, Shilpa, and A. Shobha Rani. "Image Filtering Using Anisotropic Diffusion for Brain Tumor Detection." In Applications of Parallel Data Processing for Biomedical Imaging. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2426-4.ch012.

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Brain tumor analysis is a critical aspect of medical applications, offering valuable structural and functional insights crucial for disease diagnosis. Early detection of tumors significantly enhances treatment outcomes and patient survival rates. However, the manual segmentation of numerous magnetic resonance images poses challenges due to the increased risk of human error. Therefore, there is a pressing need for computer-aided detection systems to ensure higher accuracy and faster tumor identification. In our work, we propose computer-aided techniques utilizing anisotropic diffusion filtering
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Shukla, Sakshi Deo, Pavel Denisov, and Tugtekin Turan. "Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240961.

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Recent advancements in speech-based topic segmentation have highlighted the potential of pretrained speech encoders to capture semantic representations directly from speech. Traditionally, topic segmentation has relied on a pipeline approach in which transcripts of the automatic speech recognition systems are generated, followed by text-based segmentation algorithms. In this paper, we introduce an end-to-end scheme that bypasses this conventional two-step process by directly employing semantic speech encoders for segmentation. Focused on the broadcasted news domain, which poses unique challeng
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Prabha V., Punya, and Sriraam N. "A Primitive Survey on Ultrasonic Imaging-Oriented Segmentation Techniques for Detection of Fetal Cardiac Chambers." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch074.

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Recognition of presence of fetal cardiac chambers through ultrasonic Doppler imaging poses a huge challenge for the clinical community. The four-chamber view and outflow tracts are found to be a potential identity marker for presence of all heart chambers as well as current states of fetal heart. Given the cine loop ultrasonic imaging sequences, computer-aided diagnostic tools have been developed to detect and measures the chambers through automated mode. Segmentation and region of interest identification process contribute significantly towards the presence of heart chamber and presence of ab
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Streszczenia konferencji na temat "Segmentation des pores"

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He, Chongyu, Zhiwu Xie, Yinlin Chen, and Edward A. Fox. "Nuclear Pore Segmentation in 3D FIB-SEM Images with Dynamic Cyclical Data Augmentation." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825445.

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Khelifi, Amine, Giuseppina Carannante, Nidhal Bouaynaya, and Charles Johnson. "Enhancing Rotorcraft Safety: Zero-Shot Visual Language Model for Obstacle Detection around Helipads from Satellite Imagery." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-289.

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Rotorcraft continue to experience higher fatal accident rates compared to fixed-wing aircraft, primarily due to low altitude flight operations and reduced situational awareness in complex environments. A critical factor is the limited availability of accurate, up-to-date information on helipads and surrounding obstacles - such as trees, poles, and buildings - that pose significant risks during takeoff and landing. Existing resources, including the Federal Aviation Administration's heliport registry, are often outdated and incomplete, particularly for private or state-operated sites, and fail t
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Joshi, R. M. "Self-Consistent Approximation for Porosity Segmentation." In Indonesian Petroleum Association - 46th Annual Convention & Exhibition 2022. Indonesian Petroleum Association, 2022. http://dx.doi.org/10.29118/ipa22-g-121.

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Carbonate reservoirs have been known to be a major source of hydrocarbons; it is well known that approximately 60% of the oil and 40% of the gas reserves in the world are found in carbonates, yet the understanding of the carbonate reservoir poses a significant challenge in exploration and exploitation. Presence of secondary porosity which differentiates it from clastic reservoirs brings its own set of complexity primarily owing to the poro-perm relationship. Carbonate fields in Bombay offshore (Western offshore of India) are no different. In order to understand the poro-perm complexity of one
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Wong, Vivian Wen Hui, Max Ferguson, Kincho H. Law, Yung-Tsun Tina Lee, and Paul Witherell. "Segmentation of Additive Manufacturing Defects Using U-Net." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-68885.

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Abstract Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses diffic
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Sugiarto, Bambang, Esa Prakasa, Ratih Damayanti, Gunawan, Riffa Haviani Laluma, and A. Andini Radisya Pratiwi. "Pores Segmentation Based on Active Contour Model for Automatic Wood Species Identification." In 2023 17th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2023. http://dx.doi.org/10.1109/tssa59948.2023.10366953.

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Panaitescu, C. T., K. Wu, Y. Tanino, and A. Starkey. "AI Enabled Digital Rock Technology for Larger Scale Modelling of Complex Fractured Subsurface Rocks." In SPE Offshore Europe Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215499-ms.

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Abstract Quantifying and modelling fractured subsurface rocks, characterised by their complex geometric heterogeneity, is crucial to the geo-energy transition because it helps predict flow properties in fractured systems. Multiscale Digital Rock Technology (MDRT) offers a solution to analyse comprehensive fluid flow mechanisms from the pore scale to much larger scales. In addition, artificial intelligence (AI) techniques can add significant value to geoscience workflows, automating time-consuming tasks, some even prohibitively long if done manually (such as 3D image volume labelling), and obta
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KALEEL, IBRAHIM, SOPHIE-MARIA RAUSCHER, and ARUN RAINA. "RECONSTRUCTION OF A SIC-SIC CMC MICROSTRUCTURE USING DEEP LEARNING AND ADVANCED IMAGE PROCESSING TECHNIQUE." In Proceedings for the American Society for Composites-Thirty Eighth Technical Conference. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/asc38/36681.

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The paper presents a workflow for the reconstruction of a SiC-SiC ceramic matrix composite (CMC) microstructure using advanced image processing techniques and deep learning. The objective of this research is to develop highly accurate physics-based computational models for CMCs by gaining a comprehensive understanding of the microstructural features and their impact on material properties. A workflow is presented to classify voxels into individual components and extract stochastic data for establishing microstructure-property correlations. X-ray computed tomography (CT) data of the SiC/SiC CMC
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Mohammad, Rafiq Darwis, Deepak Devegowda, Chandra Rai, Mark Curtis, Sanjana Mudduluru, and Sai Kiran Maryada. "Self-Supervised Learning Using Vision Transformer Architecture for Rock Image Segmentation." In SPE Europe Energy Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225609-ms.

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Abstract The segmentation of microstructural features in Scanning Electron Microscopy (SEM) images of shale samples is critical for petrophysical analyses, including mineralogy quantification, pore network analysis, and pore system identification. However, processing these images efficiently and accurately typically requires supervised deep learning-based methods, such as semantic segmentation algorithms. Semantic segmentation classifies each pixel in an image into a predefined category (e.g., organic material, inorganic material, pore), regardless of the number of times that category appears
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Safonov, Ilia, Anton Kornilov, and Iryna Reimers. "Rendering Semisynthetic FIB-SEM Images of Rock Samples." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-855-863.

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Digital rock analysis is a prospective approach to estimate properties of oil and gas reservoirs. This concept implies constructing a 3D digital twin of a rock sample. Focused Ion Beam - Scanning Electron Microscope (FIB-SEM) allows to obtain a 3D image of a sample at nanoscale. One of the main specific features of FIB-SEM images in case of porous media is pore-back (or shine-through) effect. Since pores are transparent, their back side is visible in the current slice, whereas, in fact, it locates in the following ones. A precise segmentation of pores is a challenging problem. Absence of annot
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Cao, Jinxin, Yiqiang Li, Yaqian Zhang, et al. "Identification of Polymer Flooding Flow Channels and Characterization of Oil Recovery Factor Based On U-Net." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2024. http://dx.doi.org/10.2118/218767-ms.

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Abstract Image identification is a major means to achieve quantitative characterization of the microscopic oil displacement process. Traditional digital image processing techniques usually uses a series of pixel-based algorithms, which is difficult to achieve real-time processing of large-scale images. Deep learning methods have the characteristics of fast speed and high accuracy. This paper proposes a four-channel image segmentation method based on RGB color and rock particle mask. First, the micro model rock particle mask is divided together with the RGB component to form four-channel input
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Raporty organizacyjne na temat "Segmentation des pores"

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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—su
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