Journal articles: 'Reconstruction des mésons D'

1

Rajesh, A., M. Aslam, and K. Jeyapalan. "2-D and 3-D reconstruction for tracheal stenosis." Anaesthesia 55, no. 5 (May 29, 2000): 489–518. http://dx.doi.org/10.1046/j.1365-2044.2000.01425-39.x.

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Nan, Liangliang, Andrei Sharf, and Baoquan Chen. "2D-D Lifting for Shape Reconstruction." Computer Graphics Forum 33, no. 7 (October 2014): 249–58. http://dx.doi.org/10.1111/cgf.12493.

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Kim, Hansung, Jean-Yves Guillemaut, Takeshi Takai, Muhammad Sarim, and Adrian Hilton. "Outdoor Dynamic 3-D Scene Reconstruction." IEEE Transactions on Circuits and Systems for Video Technology 22, no. 11 (November 2012): 1611–22. http://dx.doi.org/10.1109/tcsvt.2012.2202185.

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WEMYSS, Michael. "Reconstruction algebras of type D (II)." Hokkaido Mathematical Journal 42, no. 2 (February 2013): 293–329. http://dx.doi.org/10.14492/hokmj/1372859589.

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Wemyss, Michael. "Reconstruction algebras of type D (I)." Journal of Algebra 356, no. 1 (April 2012): 158–94. http://dx.doi.org/10.1016/j.jalgebra.2012.01.019.

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Cifor, Amalia, Li Bai, and Alain Pitiot. "Smoothness-guided 3-D reconstruction of 2-D histological images." NeuroImage 56, no. 1 (May 2011): 197–211. http://dx.doi.org/10.1016/j.neuroimage.2011.01.060.

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Marabini, R., C. O. S. Sorzano, S. Matej, J. J. Fernandez, J. M. Carazo, and G. T. Herman. "3-D Reconstruction of 2-D Crystals in Real Space." IEEE Transactions on Image Processing 13, no. 4 (April 2004): 549–61. http://dx.doi.org/10.1109/tip.2003.822620.

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Blinov, A., and M. Petrou. "Reconstruction of 3-D horizons from 3-D seismic datasets." IEEE Transactions on Geoscience and Remote Sensing 43, no. 6 (June 2005): 1421–31. http://dx.doi.org/10.1109/tgrs.2005.844731.

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Kiberstis, Paula A. "Metastases undergo reconstruction." Science 357, no. 6346 (July 6, 2017): 43.4–43. http://dx.doi.org/10.1126/science.357.6346.43-d.

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Vanezis, P., M. Vanezis, G. McCombe, and T. Niblett. "Facial reconstruction using 3-D computer graphics." Forensic Science International 108, no. 2 (February 2000): 81–95. http://dx.doi.org/10.1016/s0379-0738(99)00026-2.

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Oliveira, Manuel, N. Sooraj Hussain, A. G. Dias, M. A. Lopes, Luís Azevedo, Horácio Zenha, Horácio Costa, and J. D. Santos. "3-D biomodelling technology for maxillofacial reconstruction." Materials Science and Engineering: C 28, no. 8 (December 2008): 1347–51. http://dx.doi.org/10.1016/j.msec.2008.02.007.

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Jiann-Der Lee, Chung-Hsien Huang, and Shih-Tseng Lee. "Improving stereotactic surgery using 3-D reconstruction." IEEE Engineering in Medicine and Biology Magazine 21, no. 6 (November 2002): 109–16. http://dx.doi.org/10.1109/memb.2002.1175146.

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Hsu, Gee-Sern Jison, Yu-Lun Liu, Hsiao-Chia Peng, and Po-Xun Wu. "RGB-D-Based Face Reconstruction and Recognition." IEEE Transactions on Information Forensics and Security 9, no. 12 (December 2014): 2110–18. http://dx.doi.org/10.1109/tifs.2014.2361028.

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Begon, Mickaël, and Patrick Lacouture. "Accuracy of 3-D Reconstruction with Occlusions." Journal of Applied Biomechanics 26, no. 1 (February 2010): 104–8. http://dx.doi.org/10.1123/jab.26.1.104.

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A marker has to be seen by at least two cameras for its three-dimensional (3-D) reconstruction, and the accuracy can be improved with more cameras. However, a change in the set of cameras used in the reconstruction can alter the kinematics. The purpose of this study was to quantify the harmful effect of occlusions on two-dimensional (2-D) images and to make recommendations about the signal processing. A reference kinematics data set was collected for a three degree-of-freedom linkage with three cameras of a commercial motion analysis system without any occlusion on the 2-D images. In the 2-D images, some occlusions were artificially created based on trials of real cyclic motions. An interpolation of 2-D trajectories before the 3-D reconstruction and two filters (Savitsky–Golay and Butterworth filters) after reconstruction were successively applied to minimize the effect of the 2-D occlusions. The filter parameters were optimized by minimizing the root mean square error between the reference and the filtered data. The optimal parameters of the filters were marker dependent, whereas no filter was necessary after a 2-D interpolation. As the occlusions cause systematic error in the 3-D reconstruction, the interpolation of the 2-D trajectories is more appropriate than filtering the 3-D trajectories.

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Tayama, Niro, Takehiko Harada, Masashi Sugasawa, and Tetsuo Senba. "3-D reconstruction of the temporal bone." Practica Oto-Rhino-Laryngologica 81, no. 1 (1988): 131–35. http://dx.doi.org/10.5631/jibirin.81.131.

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Voisin, Vanessa. "Karl D. Qualls, From Ruins to Reconstruction." Cahiers du monde russe 51, no. 51/4 (November 25, 2010): 737–40. http://dx.doi.org/10.4000/monderusse.7400.

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Whitehead, L. H., and A. J. Perch. "The CHIPS R&D Programme: Reconstruction." Journal of Physics: Conference Series 888 (September 2017): 012153. http://dx.doi.org/10.1088/1742-6596/888/1/012153.

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Lv, Zhihan, Jaime Lloret Mauri, and Houbing Song. "Editorial RGB-D Sensors and 3D Reconstruction." IEEE Sensors Journal 20, no. 20 (October 15, 2020): 11751–52. http://dx.doi.org/10.1109/jsen.2020.3015417.

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Chung, Ronald, and Chi-kin Ho. "3-D Reconstruction from Tomographic Data Using 2-D Active Contours." Computers and Biomedical Research 33, no. 3 (June 2000): 186–210. http://dx.doi.org/10.1006/cbmr.2000.1541.

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20

Shima, Hiromasa. "2-D and 3-D resistivity image reconstruction using crosshole data." GEOPHYSICS 57, no. 10 (October 1992): 1270–81. http://dx.doi.org/10.1190/1.1443195.

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Theoretical changes in the distribution of electrical potential near subsurface resistivity anomalies have been studied using two resistivity models. The results suggest that the greatest response from such anomalies can be observed with buried electrodes, and that the resistivity model of a volume between boreholes can be accurately reconstructed by using crosshole data. The distributive properties of crosshole electrical potential data obtained by the pole‐pole array method have also been examined using the calculated partial derivative of the observed apparent resistivity with respect to a small cell within a given volume. The results show that for optimum two‐dimensional (2-D) and three‐dimensional (3-D) target imaging, in‐line data and crossline data should be combined, and an area outside the zone of exploration should be included in the analysis. In this paper, the 2-D and 3-D resistivity images presented are reconstructed from crosshole data by the combination of two inversion algorithms. The first algorithm uses the alpha center method for forward modeling and reconstructs a resistivity model by a nonlinear least‐squares inversion. Alpha centers express a continuously varying resistivity model, and the distribution of the electrical potential from the model can be calculated quickly. An initial general model is determined by the resistivity backprojection technique (RBPT) prior to the first inversion step. The second process uses finite elements and a linear inversion algorithm to improve the resolution of the resistivity model created by the first step. Simple 2-D and 3-D numerical models are discussed to illustrate the inversion method used in processing. Data from several field studies are also presented to demonstrate the capabilities of using crosshole resistivity exploration techniques. The numerical experiments show that by using the combined reconstruction algorithm, thin conductive layers can be imaged with good resolution for 2-D and 3-D cases. The integration of finite‐element computations is shown to improve the image obtained by the alpha center inversion process for 3-D applications. The first field test uses horizontal galleries to evaluate complex 2-D features of a zinc mine. The second field test illustrates the use of three boreholes at a dam site to investigate base rock features and define the distribution of an altered zone in three dimensions.

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Duszak, Richard. "The New Reconstruction Codes: 3-D is Better Than No D." Journal of the American College of Radiology 3, no. 1 (January 2006): 67–68. http://dx.doi.org/10.1016/j.jacr.2005.09.017.

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22

Samora, Julie Balch, and Ryan D. Klinefelter. "Flexor Tendon Reconstruction." Journal of the American Academy of Orthopaedic Surgeons 24, no. 1 (January 2016): 28–36. http://dx.doi.org/10.5435/jaaos-d-14-00195.

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23

Mueller, K., and R. Yagel. "Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware." IEEE Transactions on Medical Imaging 19, no. 12 (2000): 1227–37. http://dx.doi.org/10.1109/42.897815.

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24

Barrett, T. A. "Reconstruction with noisy data." Proceedings, annual meeting, Electron Microscopy Society of America 44 (August 1986): 186–87. http://dx.doi.org/10.1017/s0424820100142554.

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The problem of deducing the 3-D structure of an object given a limited number of 2-D projections (e.g., STEM micrographs) is well-known. It is an ill-posed problem in the sense that very many solutions exist that have the same 2-D projections. If the data are assumed to be noisy (most STEM images are very noisy), then the problem is still ill-posed: many solutions exist that yield the same least-square error from the data.Crewe et al. have shown that, remarkably, the constraint that many objects are essentially Boolean in nature (have constant density) means that their structure can be determined very well even with very few projections, if there is little noise.

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Garamvölgyi, Dániel, and Tibor Jordán. "Graph Reconstruction from Unlabeled Edge Lengths." Discrete & Computational Geometry 66, no. 1 (February 26, 2021): 344–85. http://dx.doi.org/10.1007/s00454-021-00275-7.

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AbstractA d-dimensional framework is a pair (G, p), where $$G=(V,E)$$ G = ( V , E ) is a graph and p is a map from V to $$\mathbb {R}^d$$ R d . The length of an edge $$uv\in E$$ u v ∈ E in (G, p) is the distance between p(u) and p(v). The framework is said to be globally rigid in $$\mathbb {R}^d$$ R d if every other d-dimensional framework (G, q), in which the corresponding edge lengths are the same, is congruent to (G, p). In a recent paper Gortler, Theran, and Thurston proved that if every generic framework (G, p) in $$\mathbb {R}^d$$ R d is globally rigid for some graph G on $$n\ge d+2$$ n ≥ d + 2 vertices (where $$d\ge 2$$ d ≥ 2 ), then already the set of (unlabeled) edge lengths of a generic framework (G, p), together with n, determine the framework up to congruence. In this paper we investigate the corresponding unlabeled reconstruction problem in the case when the above generic global rigidity property does not hold for the graph. We provide families of graphs G for which the set of (unlabeled) edge lengths of any generic framework (G, p) in d-space, along with the number of vertices, uniquely determine the graph, up to isomorphism. We call these graphs weakly reconstructible. We also introduce the concept of strong reconstructibility; in this case the labeling of the edges is also determined by the set of edge lengths of any generic framework. For $$d=1,2$$ d = 1 , 2 we give a partial characterization of weak reconstructibility as well as a complete characterization of strong reconstructibility of graphs. In particular, in the low-dimensional cases we describe the family of weakly reconstructible graphs that are rigid but not redundantly rigid.

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26

Pappalardo, Vincenzo, Stefano La Rosa, Andrea Imperatori, Nicola Rotolo, Maria Laura Tanda, Andrea Sessa, Lorenzo Dominioni, and Gianlorenzo Dionigi. "CT airways 3-D reconstruction showing tracheal stenosis." ASVIDE 3 (October 2016): 402. http://dx.doi.org/10.21037/asvide.2016.402.

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27

Lin, Nan, Jing Lin, and Ni Lin. "Reverse 3 D Garment Model Reconstruction Technology Research." Advanced Materials Research 479-481 (February 2012): 2148–51. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.2148.

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Reverse Engineering technology has became a widely applied technology, but it has never been used in the field of Clothing Design Engineering .This paper aims to explore how to constructing the three-dimensional entity model of clothing by Geomagic software, then introduce the files into Freeform software in order to redesign clothing. The matters need attention during the Point cloud select, t data processing and model reconstruction are described in detail. In addition, the paper discusses the best solution of importing into Freeform software in different situation, also discusses the processing procedure after importing.

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Üçoluk, Göktürk, and I. Hakkı Toroslu. "Automatic reconstruction of broken 3-D surface objects." Computers & Graphics 23, no. 4 (August 1999): 573–82. http://dx.doi.org/10.1016/s0097-8493(99)00075-8.

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29

Lin, Huei-Yung. "Vision system for fast 3-D model reconstruction." Optical Engineering 43, no. 7 (July 1, 2004): 1651. http://dx.doi.org/10.1117/1.1758731.

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Wang, Kangkan, Guofeng Zhang, and Hujun Bao. "Robust 3D Reconstruction With an RGB-D Camera." IEEE Transactions on Image Processing 23, no. 11 (November 2014): 4893–906. http://dx.doi.org/10.1109/tip.2014.2352851.

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31

Johnson, C. A., J. Seidel, and A. Sofer. "Interior-point methodology for 3-D PET reconstruction." IEEE Transactions on Medical Imaging 19, no. 4 (April 2000): 271–85. http://dx.doi.org/10.1109/42.848179.

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32

Lyroudia, K., G. Mikrogeorgis, N. Nikopoulos, G. Samakovitis, I. Molyvdas, and I. Pitas. "Computerized 3-D reconstruction of two "double teeth"." Dental Traumatology 13, no. 5 (October 1997): 218–22. http://dx.doi.org/10.1111/j.1600-9657.1997.tb00043.x.

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33

Sanders, Toby, John D. Roehling, K. Joost Batenburg, Bruce C. Gates, Alexander Katz, Peter Binev, and Ilke Arslan. "Advanced 3-D Reconstruction Algorithms for Electron Tomography." Microscopy and Microanalysis 20, S3 (August 2014): 794–95. http://dx.doi.org/10.1017/s1431927614005698.

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34

Silva, Moacyr, Svetlana Grinblat, and Sheldon C. Sommers. "3-D Reconstruction of Endometrial Carcinoma In Situ." American Journal of Clinical Pathology 86, no. 4 (October 1, 1986): 493–98. http://dx.doi.org/10.1093/ajcp/86.4.493.

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35

Herman, G. T. "Computerized Reconstruction and 3-D Imaging in Medicine." Annual Review of Computer Science 1, no. 1 (June 1986): 153–79. http://dx.doi.org/10.1146/annurev.cs.01.060186.001101.

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Pap, George S. "Hidalgo, D. A. Fibula free flap mandibular reconstruction." Plastic and Reconstructive Surgery 96, no. 2 (August 1995): 506. http://dx.doi.org/10.1097/00006534-199508000-00070.

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Ju, Z. W., E. C. Frey, and B. M. W. Tsui. "Distributed 3-D iterative reconstruction for quantitative SPECT." IEEE Transactions on Nuclear Science 42, no. 4 (1995): 1301–9. http://dx.doi.org/10.1109/23.467865.

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38

Prosmans, Fabienne, and Jean-Pierre Schneiders. "A topological reconstruction theorem for $\mathscr {D}$∞-modules." Duke Mathematical Journal 102, no. 1 (March 2000): 39–86. http://dx.doi.org/10.1215/s0012-7094-00-10212-8.

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39

Louis, A. K., and P. Maass. "Contour reconstruction in 3-D X-ray CT." IEEE Transactions on Medical Imaging 12, no. 4 (1993): 764–69. http://dx.doi.org/10.1109/42.251129.

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40

Wanat, Monika Anna, Maura Malinska, and Krzysztof Wozniak. "Reconstruction of charge density of vitamin D analogues." Acta Crystallographica Section A Foundations and Advances 73, a2 (December 1, 2017): C704. http://dx.doi.org/10.1107/s2053273317088702.

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41

Lao, L. L., H. E. St John, Q. Peng, J. R. Ferron, E. J. Strait, T. S. Taylor, W. H. Meyer, C. Zhang, and K. I. You. "MHD Equilibrium Reconstruction in the DIII-D Tokamak." Fusion Science and Technology 48, no. 2 (October 2005): 968–77. http://dx.doi.org/10.13182/fst48-968.

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42

Riquelme, Adrián, Roberto Tomás, Miguel Cano, José Luis Pastor, Brian Gootee, and Joseph P. Cook. "Reconstruction of earth fissures 3-D from videos." Proceedings of the International Association of Hydrological Sciences 382 (April 22, 2020): 677–81. http://dx.doi.org/10.5194/piahs-382-677-2020.

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Abstract. Earth fissures are pervasive cracks that develop on valley floors as a consequence of land subsidence associated with extensive groundwater withdrawal. To capture geometrical, geological and geotechnical information of ground fissures is of paramount importance for their characterization. Recent advances in remote sensing techniques and the accessibility to remotely piloted aircraft systems (RPAS) as well as the evolution of onboard digital cameras enable the capture of digital photos and videos. Using digital photos along with the Structure from Motion (SfM) technique and following certain strategies, we can reconstruct a 3-D model of the earth fissures under study. This technique requires digital photos, but when a digital video is available, we can convert it into a set of frames and equally apply the procedure. Besides, the extraction of frames from a video assures a key condition for the SfM technique: the overlap between photos. The resulting 3-D model should be scaled and oriented using a rigid transformation matrix or even better including ground control points (GCP) into the captured photos or frames. The latter enables the geo-referencing of the point cloud and the correction of linear and non-linear deformations. In this work, the proposed methodology is illustrated through the application of SfM technique to a high-resolution video downloaded from YouTube (i.e. https://youtu.be/9xdAnftBKvY, last access: 20 February 2020). The video shows a mile-long earth fissure that appeared sometime between March 2014 and December 2014 near the Tator Hills (Arizona, USA) over Quaternary sediments. The Arizona Geological Survey captured these videos using an RPAS. The frames of the video were downloaded and extracted using a simple Matlab code. Then, we sub-sampled the frames and processed them using the software Agisoft Metashape Professional. Finally, we got metric data from Google Earth and generated a 3-D model. The quality of the 3-D model strongly depends on the quality of the photos and the GCP. However, this study shows the potential of this technique, instrumentation and data available on Internet for the development of 3-D point clouds and 3-D models for the detailed analysis of earth fissures.

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Kengla, Carlos, Hyun Wook Kang, John D. Jackson, Sang Jin Lee, James Yoo, and Anthony Atala. "3-D integrated organ printing for ear reconstruction." Journal of the American College of Surgeons 221, no. 4 (October 2015): e26. http://dx.doi.org/10.1016/j.jamcollsurg.2015.08.365.

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44

Yang, Allen Y., Kun Huang, Shankar Rao, Wei Hong, and Yi Ma. "Symmetry-based 3-D reconstruction from perspective images." Computer Vision and Image Understanding 99, no. 2 (August 2005): 210–40. http://dx.doi.org/10.1016/j.cviu.2005.01.004.

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45

Aschwanden, Markus J., and Jean-Pierre Wülser. "3-D reconstruction of active regions with STEREO." Journal of Atmospheric and Solar-Terrestrial Physics 73, no. 10 (June 2011): 1082–95. http://dx.doi.org/10.1016/j.jastp.2010.09.008.

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46

Wang, Y. F., and J. K. Aggarwal. "Surface reconstruction and representation of 3-D scenes." Pattern Recognition 19, no. 3 (January 1986): 197–207. http://dx.doi.org/10.1016/0031-3203(86)90010-5.

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47

Geist, Daniel, and Michael W. Vannier. "PC-based 3-D reconstruction of medical images." Computers & Graphics 13, no. 2 (January 1989): 135–43. http://dx.doi.org/10.1016/0097-8493(89)90055-1.

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48

Peschel, O., U. Szeimies, C. Vollmar, and S. Kirchhoff. "Postmortem 3-D reconstruction of skull gunshot injuries." Forensic Science International 233, no. 1-3 (December 2013): 45–50. http://dx.doi.org/10.1016/j.forsciint.2013.08.012.

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İmre, Evren, Sebastian Knorr, Burak Özkalaycı, Uğur Topay, A. Aydın Alatan, and Thomas Sikora. "Towards 3-D scene reconstruction from broadcast video." Signal Processing: Image Communication 22, no. 2 (February 2007): 108–26. http://dx.doi.org/10.1016/j.image.2006.11.011.

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Allen, D., and A. D. B. Chant. "QALYfied arterial reconstruction." Lancet 339, no. 8801 (May 1992): 1117. http://dx.doi.org/10.1016/0140-6736(92)90713-d.

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Journal articles on the topic 'Reconstruction des mésons D'

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