scholarly journals Optimal Design of Intracranial Hematoma Puncture Drainage Tube Based on Adaptive Bifurcation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jincai Chang ◽  
Liyan Jia ◽  
Fei Yu ◽  
Xinghui Hao ◽  
Ze Lu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Three Dimensional ◽  
Drainage Tube ◽  
Tube Model ◽  
Intracranial Hematoma ◽  
Extraction Algorithm ◽  
Accurate Dose ◽  
Multiple Absorption ◽  
Ct Data

Aiming at the puncture and drainage of clinical intracranial hematoma, we proposed an adaptive bifurcation algorithm based on the hematoma point cloud and optimized the design of the drainage tube. Firstly, based on the CT data of intracranial hematoma patients, a three-dimensional hematoma model was established, the point cloud on the surface of the hematoma was extracted and simplified, and the location of the main drainage tube was located by using the long-axis extraction algorithm. Secondly, the Eight Diagrams algorithm was used to identify the internal point cloud of hematoma, and the positions of multiple absorption points were determined by the K-means clustering algorithm. The locations of the bifurcation points of the main drainage tubes were calculated by the numerical method, and the telescopic lengths and directions of multiple subdrainage tubes were obtained. Finally, connect the main tube and the subtube, design an adaptive bifurcation drainage tube model, and apply it to intracranial hematoma puncture and drainage surgery. The algorithm can accurately determine the puncture point, puncture path, number, and location of subdrainage tubes according to the geometric characteristics of hematoma, achieve a uniform and accurate dose adjustment and drainage of intracranial hematoma, and accelerate the dissolution and drainage speed. The application of an adaptive bifurcation drainage tube can significantly reduce the risk of intracerebral hemorrhage, intracranial infection, and other complications, which has certain guiding significance and application value in clinical practice.

The Trees Skeleton Extraction Based on Point Cloud Contraction

2013 ◽  
Vol 475-476 ◽  
pp. 355-360
Author(s):  
Gui Zhen He
Keyword(s):  
Point Cloud ◽  
Undirected Graph ◽  
Three Dimensional ◽  
Three Dimensional Model ◽  
Extraction Algorithm ◽  
Point Set ◽  
Dimensional Simulation ◽  
Curve Skeleton ◽  
Edge Collapse ◽  
Curve Form

To achieve the trees three dimensional simulation, the most critical step is to extract the trees skeleton. This paper focuses on the point cloud contraction-based skeletal extraction algorithm, uses neighbors to build a transformation matrix, extract a discrete point set approximated to the real skeleton by Laplacian contraction, and constructs a 1D curve skeleton with the help of a weighted undirected graph and edge collapse algorithm. 1D curve form is more easy to operate, guide the reconstruction of three-dimensional model, solve the problem of incomplete data in the process of modeling .

scholarly journals Reconstruction of Two-dimensional Image to Three-dimensional Point Cloud Data for Detection of Empty Rot Defects in Tree Growth

CONVERTER ◽  
2021 ◽  
pp. 459-470
Author(s):  
Shufeng Jiang, Keqi Wang
Keyword(s):  
Point Cloud ◽  
Color Space ◽  
Three Dimensional ◽  
Boundary Curve ◽  
Data Reconstruction ◽  
Two Dimensional ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Extraction Algorithm ◽  
Radar Wave

In the application of nondestructive detecting of trees, it is a technical problem to use radar waves to detect tree specimens with growth defects, how to segment defect areas after obtaining two-dimensional images, and reverse simulate the detection results with three-dimensional point cloud data. Therefore, the method of extracting boundary information according to color features is studied to extract the boundary curve of empty rot area, and the selection of higher precision extraction algorithm is determined by comparing the boundary extraction results of HSV color space and RGB color space in laboratory According to the extracted void boundary curve, the reverse modeling is carried out, and the mapping from 2D inspection gray image to 3D space is realized, The point cloud data reconstruction needed for 3D modeling of multi-curved surfaces is obtained in reverse. The boundary curve extraction algorithm in this study is used to process the images of nondestructive testing of trees. Through comparative experiments and error analysis, the accurate modeling conclusion from inversion of 2D images to 3D point cloud data reconstruction by radar wave detection is verified, and the Core issue problem of point cloud reconstruction in the ill-conditioned area of tree growth and decay detected by radar wave is solved.

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Research on the university intelligent learning analysis system based on AI

2021 ◽  
pp. 1-10
Author(s):  
Meng Huang ◽  
Shuai Liu ◽  
Yahao Zhang ◽  
Kewei Cui ◽  
Yana Wen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Academic Performance ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Three Dimensional ◽  
Training Model ◽  
Future Trend ◽  
Artificial Intelligence Technology ◽  
Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

scholarly journals Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 741 ◽  
Author(s):  
Haihui Yang ◽  
Xiaochan Wang ◽  
Guoxiang Sun
Keyword(s):  
Intelligent Control ◽  
Point Cloud ◽  
Three Dimensional ◽  
Fruit Tree ◽  
Tree Canopy ◽  
Morphological Parameters ◽  
Kinect Sensor ◽  
Self Calibration ◽  
Morphological Measurement ◽  
Tree Width

Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.

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