Auswahl der wissenschaftlichen Literatur zum Thema „Segmentation des pores“
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Zeitschriftenartikel zum Thema "Segmentation des pores"
Sintorn, Ida-Maria, Stina Svensson, Maria Axelsson und Gunilla Borgefors. „Segmentation of individual pores in 3D paper images“. Nordic Pulp & Paper Research Journal 20, Nr. 3 (01.08.2005): 316–19. http://dx.doi.org/10.3183/npprj-2005-20-03-p316-319.
Der volle Inhalt der QuelleBauer, Benjamin, Xiaohao Cai, Stephan Peth, Katja Schladitz und Gabriele Steidl. „Variational-based segmentation of bio-pores in tomographic images“. Computers & Geosciences 98 (Januar 2017): 1–8. http://dx.doi.org/10.1016/j.cageo.2016.09.013.
Der volle Inhalt der QuelleLiu, Lei, Qiaoling Han, Yue Zhao und Yandong Zhao. „A Novel Method Combining U-Net with LSTM for Three-Dimensional Soil Pore Segmentation Based on Computed Tomography Images“. Applied Sciences 14, Nr. 8 (16.04.2024): 3352. http://dx.doi.org/10.3390/app14083352.
Der volle Inhalt der QuelleYosifov, Miroslav, Patrick Weinberger, Bernhard Plank, Bernhard Fröhler, Markus Hoeglinger, Johann Kastner und Christoph Heinzl. „Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network“. Acta Polytechnica CTU Proceedings 42 (12.10.2023): 87–93. http://dx.doi.org/10.14311/app.2023.42.0087.
Der volle Inhalt der QuelleTomažinčič, Dejan, Žiga Virk, Peter Marijan Kink, Gregor Jerše und 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, Nr. 1 (31.12.2020): 72. http://dx.doi.org/10.3390/met11010072.
Der volle Inhalt der QuelleTong, Tong, Yan Cai, Da Wei Sun und 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.
Der volle Inhalt der QuelleYoon, Huisu, Semin Kim, Jongha Lee und Sangwook Yoo. „Deep-Learning-Based Morphological Feature Segmentation for Facial Skin Image Analysis“. Diagnostics 13, Nr. 11 (29.05.2023): 1894. http://dx.doi.org/10.3390/diagnostics13111894.
Der volle Inhalt der QuelleSong, Wenlong, Junyu Li, Kexin Li, Jingxu Chen und Jianping Huang. „An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model“. Forests 11, Nr. 9 (01.09.2020): 954. http://dx.doi.org/10.3390/f11090954.
Der volle Inhalt der QuelleSoboleva, N. N., und A. N. Mushnikov. „Determination of the volume fraction of primary carbides in the microstructure of composite coatings using semantic segmentation“. Frontier materials & technologies, Nr. 3 (2023): 95–102. http://dx.doi.org/10.18323/2782-4039-2023-3-65-9.
Der volle Inhalt der QuelleWen, Hao, Chang Huang und Shengmin Guo. „The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts“. Materials 14, Nr. 10 (15.05.2021): 2575. http://dx.doi.org/10.3390/ma14102575.
Der volle Inhalt der QuelleDissertationen zum Thema "Segmentation des pores"
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.
Der volle Inhalt der QuelleDing, Nan. „3D Modeling of the Lamina Cribrosa in OCT Data“. Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS148.
Der volle Inhalt der QuelleThe lamina cribrosa (LC) is a 3D collagenous mesh in theoptic nerve head that plays a crucial role in themechanisms and diagnosis of glaucoma, the second leading cause of blindness in the world. The LC is composed of so-called “pores”, namely axonal paths within the collagenous mesh, through which the axons pass to reach the brain. In vivo 3D observation of the LC pores is now possible thanks to advances in Optical Coherence Tomography (OCT) technology. In this study, we aim to automatically perform the 3D reconstruction of pore paths from OCT volumes, in order to study the remodeling of the lamina cribrosa during glaucoma and better understand this disease.The limited axial resolution of conventional OCT as well as the low signal to noise ratio (SNR) poses challenges for the robust characterization of axonal paths with enough reliability, knowing that it is difficult even for experts to identify the pores in a single en-face image. To this end, our first contribution introduces an innovative method to register and fuse 2 orthogonal 3D OCT volumes in order to enhance the pores. This is, to our knowledge, the first time that orthogonal OCT volumes are jointly exploited to achieve better image quality. Experimental results demonstrate that our algorithm is robust and leads to accurate alignment.Our second contribution presents a context-aware attention U-Net method, a deep learning approach using partial points annotation for the accurate pore segmentation in every 2D en-face image. This work is also, to the best of our knowledge, the first attempt to look into the LC pore reconstruction problem using deep learning methods. Through a comparative analysis with other state-of-the-art methods, we demonstrate the superior performance of the proposed approach.Our robust and accurate pore registration and segmentation methods provide a solid foundation for 3D reconstruction of axonal pathways, our third contribution. We propose a pore tracking method based on a locally applied parametric active contour algorithm. Our model integrates the characteristics of low intensity and regularity of pores. Combined with the 2D segmentation maps, it enables us to reconstruct the axonal paths in 3D plane by plane. These results pave the way for the calculation of biomarkers characterizing the LC and facilitate medical interpretation
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.
Der volle Inhalt der QuelleBuchteile zum Thema "Segmentation des pores"
Jiqun, Zhang, Hu Chungjin, Liu Xin, He Dongmei und Li Hua. „An Algorithm for Rock Pore Image Segmentation“. In Lecture Notes in Electrical Engineering, 243–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46578-3_28.
Der volle Inhalt der QuelleJiang, Hao. „Finding Human Poses in Videos Using Concurrent Matching and Segmentation“. In Computer Vision – ACCV 2010, 228–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19315-6_18.
Der volle Inhalt der QuelleKrüger, Nina, Jan Brüning, Leonid Goubergrits, Matthias Ivantsits, Lars Walczak, Volkmar Falk, Henryk Dreger, Titus Kühne und Anja Hennemuth. „Deep Learning-Based Pulmonary Artery Surface Mesh Generation“. In Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers, 140–51. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52448-6_14.
Der volle Inhalt der QuelleLu, Siwei, Xiaofang Zhao, Huazhu Liu und 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.
Der volle Inhalt der QuelleNandhitha, N. M., S. Emalda Roslin, Rekha Chakravarthi und 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.
Der volle Inhalt der QuelleHiremath, Shilpa, und A. Shobha Rani. „Image Filtering Using Anisotropic Diffusion for Brain Tumor Detection“. In Applications of Parallel Data Processing for Biomedical Imaging, 244–60. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2426-4.ch012.
Der volle Inhalt der QuellePrabha V., Punya, und 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, 1455–66. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch074.
Der volle Inhalt der QuelleZhang, Yan. „A New Method for Improving the Accuracy of Word Segmentation in Modern Chinese Texts“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231409.
Der volle Inhalt der QuelleWin, Htwe Pa Pa, Phyo Thu Thu Khine und Khin Nwe Ni Tun. „Character Segmentation Scheme for OCR System“. In Intelligent Computer Vision and Image Processing, 262–71. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3906-5.ch018.
Der volle Inhalt der QuelleShillcock, Richard, Paul Cairns, Nick Chater und Joe Levy. „Statistical and Connectionist Modelling of the Development of Speech Segmentation“. In Models of Language Acquisition, 103–20. Oxford University PressOxford, 2000. http://dx.doi.org/10.1093/oso/9780198299899.003.0006.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Segmentation des pores"
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.
Der volle Inhalt der QuelleWong, Vivian Wen Hui, Max Ferguson, Kincho H. Law, Yung-Tsun Tina Lee und 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.
Der volle Inhalt der QuelleSugiarto, Bambang, Esa Prakasa, Ratih Damayanti, Gunawan, Riffa Haviani Laluma und 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.
Der volle Inhalt der QuellePanaitescu, C. T., K. Wu, Y. Tanino und 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.
Der volle Inhalt der QuelleKALEEL, IBRAHIM, SOPHIE-MARIA RAUSCHER und 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.
Der volle Inhalt der QuelleSafonov, Ilia, Anton Kornilov und 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.
Der volle Inhalt der QuelleCao, Jinxin, Yiqiang Li, Yaqian Zhang, Wenbin Gao, Yuling Zhang, Yifei Cai, Xuechen Tang, Qihang Li und Zheyu Liu. „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.
Der volle Inhalt der QuelleHage, Ilige S., und Ramsey F. Hamade. „Distribution of Porosity in Cortical (Bovine) Bone“. In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51703.
Der volle Inhalt der QuelleDing, Nan, Hélène Urien, Florence Rossant, Jérémie Sublime und Michel Paques. „Context-aware Attention U-Net for the segmentation of pores in Lamina Cribrosa using partial points annotation“. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00088.
Der volle Inhalt der QuelleHage, Ilige S., Mu'tasem A. Shehadeh und Ramsey F. Hamade. „Application of Homogenization Theory to Study the Mechanics of Cortical Bone“. In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36427.
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