Добірка наукової літератури з теми "Pore segmentation"
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Статті в журналах з теми "Pore segmentation"
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 (April 16, 2024): 3352. http://dx.doi.org/10.3390/app14083352.
Повний текст джерелаLi, Mingjiang, Pan Zhang, and Tao Hai. "Pore extraction method of rock thin section based on Attention U-Net." Journal of Physics: Conference Series 2467, no. 1 (May 1, 2023): 012016. http://dx.doi.org/10.1088/1742-6596/2467/1/012016.
Повний текст джерелаBerg, Steffen, Nishank Saxena, Majeed Shaik, and Chaitanya Pradhan. "Generation of ground truth images to validate micro-CT image-processing pipelines." Leading Edge 37, no. 6 (June 2018): 412–20. http://dx.doi.org/10.1190/tle37060412.1.
Повний текст джерелаYang, Eomzi, Dong Hun Kang, and Tae Sup Yun. "Reliable estimation of hydraulic permeability from 3D X-ray CT images of porous rock." E3S Web of Conferences 205 (2020): 08004. http://dx.doi.org/10.1051/e3sconf/202020508004.
Повний текст джерелаIdowu, N. A. A., C. Nardi, H. Long, T. Varslot, and P. E. E. Øren. "Effects of Segmentation and Skeletonization Algorithms on Pore Networks and Predicted Multiphase-Transport Properties of Reservoir-Rock Samples." SPE Reservoir Evaluation & Engineering 17, no. 04 (August 13, 2014): 473–83. http://dx.doi.org/10.2118/166030-pa.
Повний текст джерелаLu, An Qun, Shou Zhi Zhang, and Qian Tian. "Matlab Image Processing Technique and Application in Pore Structure Characterization of Hardened Cement Pastes." Advanced Materials Research 785-786 (September 2013): 1374–79. http://dx.doi.org/10.4028/www.scientific.net/amr.785-786.1374.
Повний текст джерелаLiu, Yifei, and Dong-Sheng Jeng. "Pore Structure of Grain-Size Fractal Granular Material." Materials 12, no. 13 (June 26, 2019): 2053. http://dx.doi.org/10.3390/ma12132053.
Повний текст джерелаZel, Ivan, Murat Kenessarin, Sergey Kichanov, Kuanysh Nazarov, Maria Bǎlǎșoiu, and Denis Kozlenko. "Pore Segmentation Techniques for Low-Resolution Data: Application to the Neutron Tomography Data of Cement Materials." Journal of Imaging 8, no. 9 (September 7, 2022): 242. http://dx.doi.org/10.3390/jimaging8090242.
Повний текст джерелаLIN, WEI, XIZHE LI, ZHENGMING YANG, LIJUN LIN, SHENGCHUN XIONG, ZHIYUAN WANG, XIANGYANG WANG, and QIANHUA XIAO. "A NEW IMPROVED THRESHOLD SEGMENTATION METHOD FOR SCANNING IMAGES OF RESERVOIR ROCKS CONSIDERING PORE FRACTAL CHARACTERISTICS." Fractals 26, no. 02 (April 2018): 1840003. http://dx.doi.org/10.1142/s0218348x18400030.
Повний текст джерелаReimers, I. A., I. V. Safonov, and I. V. Yakimchuk. "Segmentation of 3D FIB-SEM data with pore-back effect." Journal of Physics: Conference Series 1368 (November 2019): 032015. http://dx.doi.org/10.1088/1742-6596/1368/3/032015.
Повний текст джерелаДисертації з теми "Pore segmentation"
Ding, Nan. "3D Modeling of the Lamina Cribrosa in OCT Data." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS148.
Повний текст джерелаThe 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
Wagh, Ameya Yatindra. "A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1287.
Повний текст джерелаSeguin, Guillaume. "Analyse des personnes dans les films stéréoscopiques." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE021/document.
Повний текст джерелаPeople are at the center of many computer vision tasks, such as surveillance systems or self-driving cars. They are also at the center of most visual contents, potentially providing very large datasets for training models and algorithms. While stereoscopic data has been studied for long, it is only recently that feature-length stereoscopic ("3D") movies became widely available. In this thesis, we study how we can exploit the additional information provided by 3D movies for person analysis. We first explore how to extract a notion of depth from stereo movies in the form of disparity maps. We then evaluate how person detection and human pose estimation methods perform on such data. Leveraging the relative ease of the person detection task in 3D movies, we develop a method to automatically harvest examples of persons in 3D movies and train a person detector for standard color movies. We then focus on the task of segmenting multiple people in videos. We first propose a method to segment multiple people in 3D videos by combining cues derived from pose estimates with ones derived from disparity maps. We formulate the segmentation problem as a multi-label Conditional Random Field problem, and our method integrates an occlusion model to produce a layered, multi-instance segmentation. After showing the effectiveness of this approach as well as its limitations, we propose a second model which only relies on tracks of person detections and not on pose estimates. We formulate our problem as a convex optimization one, with the minimization of a quadratic cost under linear equality or inequality constraints. These constraints weakly encode the localization information provided by person detections. This method does not explicitly require pose estimates or disparity maps but can integrate these additional cues. Our method can also be used for segmenting instances of other object classes from videos. We evaluate all these aspects and demonstrate the superior performance of this new method
Madadi, Meysam. "Human segmentation, pose estimation and applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/457900.
Повний текст джерелаAutomatic analyzing humans in photographs or videos has great potential applications in computer vision containing medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task. In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. non-rigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin. We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions. Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures.
Chen, Daniel Chien Yu. "Image segmentation and pose estimation of humans in video." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/66230/1/Daniel_Chen_Thesis.pdf.
Повний текст джерелаSandhu, Romeil Singh. "Statistical methods for 2D image segmentation and 3D pose estimation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37245.
Повний текст джерелаDELERUE, 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.
Повний текст джерелаHewa, Thondilege Akila Sachinthani Pemasiri. "Multimodal Image Correspondence." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235433/1/Akila%2BHewa%2BThondilege%2BThesis%281%29.pdf.
Повний текст джерелаCalzavara, Ivan. "Human pose augmentation for facilitating Violence Detection in videos: a combination of the deep learning methods DensePose and VioNetHuman pose augmentation for facilitating Violence Detection in videos: a combination of the deep learning methods DensePose and VioNet." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40842.
Повний текст джерелаKarabagli, Bilal. "Vérification automatique des montages d'usinage par vision : application à la sécurisation de l'usinage." Phd thesis, Université Toulouse le Mirail - Toulouse II, 2013. http://tel.archives-ouvertes.fr/tel-01018079.
Повний текст джерелаКниги з теми "Pore segmentation"
Shiffrar, Maggie. The Aperture Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0076.
Повний текст джерелаCastellani, Claudia, and Marianne Wootton. Crustacea: Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0021.
Повний текст джерелаЧастини книг з теми "Pore segmentation"
Jiqun, Zhang, Hu Chungjin, Liu Xin, He Dongmei, and 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.
Повний текст джерелаRamon Soria, Pablo, Fouad Sukkar, Wolfram Martens, B. C. Arrue, and Robert Fitch. "Multi-view Probabilistic Segmentation of Pome Fruit with a Low-Cost RGB-D Camera." In ROBOT 2017: Third Iberian Robotics Conference, 320–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70836-2_27.
Повний текст джерелаKang, Wenrui, Xu Wang, Jixia Zhang, Xiaoming Hu, and Qin Li. "Two-Way Perceived Color Difference Saliency Algorithm for Image Segmentation of Port Wine Stains." In Communications in Computer and Information Science, 50–60. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1160-5_5.
Повний текст джерелаLu, 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.
Повний текст джерелаVetrivel, S. C., T. P. Saravanan, V. P. Arun, and R. Maheswari. "Innovative Approaches to Market Segmentation Using AI in Emerging Economies." In Advances in Marketing, Customer Relationship Management, and E-Services, 343–74. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7122-0.ch017.
Повний текст джерелаMartin, Vincent, and Monique Thonnat. "A Learning Approach for Adaptive Image Segmentation." In Scene Reconstruction Pose Estimation and Tracking. I-Tech Education and Publishing, 2007. http://dx.doi.org/10.5772/4946.
Повний текст джерелаHan, Dongil. "Real-Time Object Segmentation of the Disparity Map Using Projection-Based Region Merging." In Scene Reconstruction Pose Estimation and Tracking. I-Tech Education and Publishing, 2007. http://dx.doi.org/10.5772/4924.
Повний текст джерелаEngemann, Heiko, Shengzhi Du, Stephan Kallweit, Chuanfang Ning, and Saqib Anwar. "AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200770.
Повний текст джерелаBhandari, Vedant, Tyson Phillips, and Ross McAree. "Novel Approaches for Point Cloud Analysis with Evidential Methods: A Multifaceted Approach to Object Pose Estimation, Point Cloud Odometry, and Sensor Registration." In Point Cloud Generation and Its Applications [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004467.
Повний текст джерелаMendez, Alberto, Alicia Mora, and Ramon Barber. "Extracción de modelos 3D basado en CNN y nubes de puntos para mapeado." In XLIV Jornadas de Automática: libro de actas: Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 6, 7 y 8 de septiembre de 2023, Zaragoza, 650–60. 2023rd ed. Servizo de Publicacións. Universidade da Coruña, 2023. http://dx.doi.org/10.17979/spudc.9788497498609.655.
Повний текст джерелаТези доповідей конференцій з теми "Pore segmentation"
Wang, Hangjun, Guangqun Zhang, Hengnian Qi, and Lingfei Ma. "Multi-objective Optimization on Pore Segmentation." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.572.
Повний текст джерелаWang, Hangjun, Hengnian Qi, Wenzhu Li, Guangqun Zhang, and Paoping Wang. "A GA-based automatic pore segmentation algorithm." In the first ACM/SIGEVO Summit. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1543834.1543989.
Повний текст джерелаQi, Heng-Nian, Feng-Nong Chen, and Ling-Fei Ma. "Pore Feature Segmentation Based on Mathematical Morphology." In IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2007. http://dx.doi.org/10.1109/iecon.2007.4460248.
Повний текст джерелаSeo, Sunyong, Sangwook Yoo, Semin Kim, Daeun Yoon, and Jongha Lee. "Facial Pore Segmentation Algorithm using Shallow CNN." In 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2022. http://dx.doi.org/10.1109/cbms55023.2022.00062.
Повний текст джерелаMalathi, S., S. Uma Maheswari, and C. Meena. "Fingerprint pore extraction based on Marker controlled Watershed Segmentation." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451426.
Повний текст джерелаMa, Zongfang, Ming Duan, Chao Liu, Huawei Liu, Yiwen Wu, Weipeng Wan, and Lin Song. "Better Semantic Segmentation For 3D Printing Concrete Surface Pore Detection." In 2023 42nd Chinese Control Conference (CCC). IEEE, 2023. http://dx.doi.org/10.23919/ccc58697.2023.10239802.
Повний текст джерелаJoshi, 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.
Повний текст джерелаJagadeesh, Ajayshankar, Ghim Ping Ong, and Yu-Min Su. "Evaluation of Pervious Concrete Pore Network Properties Using Watershed Segmentation Approach." In International Airfield and Highway Pavements Conference 2019. Reston, VA: American Society of Civil Engineers, 2019. http://dx.doi.org/10.1061/9780784482469.044.
Повний текст джерелаGaviria-Hdz, Juan Fernando, Leidy Johanna Medina, Carlos Mera, Lina Chica, and Lina Maria Sepulveda-Cano. "Assessment of segmentation methods for pore detection in cellular concrete images." In 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). IEEE, 2019. http://dx.doi.org/10.1109/stsiva.2019.8730220.
Повний текст джерелаFan, Yuchen, Keyu Liu, and Lingjie Yu. "CHARACTERIZING SHALE PORE NETWORKS IN SHALES USING FIB-SEM 3D DATA BASED ON A NEW PORE-SPACE SEGMENTATION METHOD." In GSA Annual Meeting in Phoenix, Arizona, USA - 2019. Geological Society of America, 2019. http://dx.doi.org/10.1130/abs/2019am-340741.
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