Reconstruction of 3D Network Model through CT Scanning

2007 ◽  
Author(s):  
Jian Hou ◽  
ShunKang Zhang ◽  
Yubin Li
Keyword(s):  
Network Model ◽  
Ct Scanning ◽  
3D Network

3D network model of NO transport in tissue

2011 ◽  
Vol 49 (6) ◽  
pp. 633-647 ◽  
Author(s):  
Xuewen Chen ◽  
Donald G. Buerk ◽  
Kenneth A. Barbee ◽  
Patrick Kirby ◽  
Dov Jaron
Keyword(s):  
Network Model ◽  
3D Network

scholarly journals MOVING HUMAN PATH TRACKING BASED ON VIDEO SURVEILLANCE IN 3D INDOOR SCENARIOS

2016 ◽  
Vol III-4 ◽  
pp. 97-101 ◽  
Author(s):  
Yan Zhou ◽  
Sisi Zlatanova ◽  
Zhe Wang ◽  
Yeting Zhang ◽  
Liu Liu
Keyword(s):  
Video Surveillance ◽  
Network Model ◽  
Path Tracking ◽  
Depth Information ◽  
Surveillance Systems ◽  
Indoor Space ◽  
Single Person ◽  
3D Network ◽  
Indoor Scenarios ◽  
Moving Path

Video surveillance systems are increasingly used for a variety of 3D indoor applications. We can analyse human behaviour, discover and avoid crowded areas, monitor human traffic and so forth. In this paper we concentrate on use of surveillance cameras to track and reconstruct the path a person has followed. For the purpose we integrated video surveillance data with a 3D indoor model of the building and develop a single human moving path tracking method. We process the surveillance videos to detected single human moving traces; then we match the depth information of 3D scenes to the constructed 3D indoor network model and define the human traces in the 3D indoor space. Finally, the single human traces extracted from multiple cameras are connected with the help of the connectivity provided by the 3D network model. Using this approach, we can reconstruct the entire walking path. The provided experiments with a single person have verified the effectiveness and robustness of the method.

scholarly journals MOVING HUMAN PATH TRACKING BASED ON VIDEO SURVEILLANCE IN 3D INDOOR SCENARIOS

2016 ◽  
Vol III-4 ◽  
pp. 97-101
Author(s):  
Yan Zhou ◽  
Sisi Zlatanova ◽  
Zhe Wang ◽  
Yeting Zhang ◽  
Liu Liu
Keyword(s):  
Video Surveillance ◽  
Network Model ◽  
Path Tracking ◽  
Depth Information ◽  
Surveillance Systems ◽  
Indoor Space ◽  
Single Person ◽  
3D Network ◽  
Indoor Scenarios ◽  
Moving Path

Video surveillance systems are increasingly used for a variety of 3D indoor applications. We can analyse human behaviour, discover and avoid crowded areas, monitor human traffic and so forth. In this paper we concentrate on use of surveillance cameras to track and reconstruct the path a person has followed. For the purpose we integrated video surveillance data with a 3D indoor model of the building and develop a single human moving path tracking method. We process the surveillance videos to detected single human moving traces; then we match the depth information of 3D scenes to the constructed 3D indoor network model and define the human traces in the 3D indoor space. Finally, the single human traces extracted from multiple cameras are connected with the help of the connectivity provided by the 3D network model. Using this approach, we can reconstruct the entire walking path. The provided experiments with a single person have verified the effectiveness and robustness of the method.

scholarly journals High-Resolution Microstructure Analysis of Cork Spot Disordered Pear Fruit “Akizuki” (Pyrus pyrifolia Nakai) Using X-Ray CT

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhenhua Cui ◽  
Nannan Wang ◽  
Yanxin Duan ◽  
Xinrui Xu ◽  
Ran Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Ct Scanning ◽  
Economic Loss ◽  
Pyrus Pyrifolia ◽  
Pear Fruit ◽  
Long Distance ◽  
Altered Expression ◽  
X Ray ◽  
3D Network

Cork spot is one of the most damaging physiological disorders in pear fruit, causing considerable economic loss every year. However, the mechanism of cork spot occurrence requires further examination. In this study, X-ray CT scanning was applied to analyze the microstructure of pear fruit “Akizuki” (Pyrus pyrifolia), a cultivar susceptible to cork spot disorder, to elucidate the fruit texture alteration between healthy and cork spotted fruit. Results showed that cork spotted fruit had much higher porosity (9.37%) than healthy fruit (3.52%). Reconstructed three-dimensional (3D) network skeleton models showed highly branched pore channels in cork spotted fruit and a low degree of pore connectivity in healthy fruit. Even in areas of disordered fruit without cork spot, the pore throat diameter, pore length, and coordinated core number (i.e., 77, 160, and 16, respectively) were much higher than that of healthy fruit. The structure analysis of fruit core showed that core deformation only occurred in cork spotted fruit. A much more highly branched network was observed in cork spotted fruit cores compared with healthy fruit cores. High-resolution observation of flesh tissue directly demonstrated that pore size in cork spotted fruit (87 μm) was four times larger than that of healthy fruit (22 μm). Altered expression of genes related to Ca2+ transport and the uneven distribution of intracellular Ca2+ were also shown to associate with the development of cork spot disorder. Our results suggest that flesh tissue damage likely occurred prior to the initiation of cork spot. The dysfunction of long-distance and transmembrane Ca2+ transport channels could be responsible for the imbalanced distribution of Ca2+ inside the fruit, thus resulting in the development of cork spot.

MODELING THE DIELECTRIC RESPONSE IN HETEROGENEOUS MATERIALS USING 3D RC NETWORKS

2009 ◽  
Vol 23 (25) ◽  
pp. 3023-3033 ◽  
Author(s):  
MING HUANG ◽  
JINGJING YANG ◽  
ZHE XIAO ◽  
JUN SUN ◽  
JINHUI PENG
Keyword(s):  
Percolation Threshold ◽  
Network Model ◽  
Dielectric Response ◽  
Heterogeneous Materials ◽  
Large Networks ◽  
3D Network ◽  
Bond Network ◽  
2D And 3D ◽  
Rc Networks ◽  
Good Agreement

A model of 3D RC networks was developed to describe the dielectric response of heterogeneous materials and was compared with the results of the 2D RC network model. We show that the "universal dielectric response" (UDR) of heterogeneous materials is a common feature of both 2D and 3D very large networks with randomly positioned resistors and capacitors, and that the percolation threshold of the 2D and 3D bond network are close to 0.5 and 0.25, respectively. In addition, it was found that the percolation threshold of the 3D network is in good agreement with the result of the coherent potential (CP) formula.

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