scholarly journals Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

2016 ◽  
Author(s):  
Ke Li ◽  
Chuyang Ye ◽  
Zhen Yang ◽  
Aaron Carass ◽  
Sarah H. Ying ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Outlier Detection ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Cerebellar Peduncles

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

2021 ◽  
Author(s):  
Haruka Kamachi ◽  
Takumi Kondo ◽  
Tahera Hossain ◽  
Anna Yokokubo ◽  
Guillaume Lopez
Keyword(s):  
Automatic Segmentation ◽  
Bone Conduction ◽  
Segmentation Method

scholarly journals An automatic segmentation method for heart sounds

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Qingshu Liu ◽  
Xiaomei Wu ◽  
Xiaojing Ma
Keyword(s):  
Automatic Segmentation ◽  
Heart Sounds ◽  
Segmentation Method

Validation of an automatic segmentation method to detect vertebral interfaces in ultrasound images

2016 ◽  
Vol 10 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Matteo Aventaggiato ◽  
Maurizio Muratore ◽  
Paola Pisani ◽  
Aimè Lay-Ekuakille ◽  
Francesco Conversano ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Images ◽  
Segmentation Method

scholarly journals An Adaptive Approach for Voltage Sag Automatic Segmentation

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3519 ◽  
Author(s):  
Xianyong Xiao ◽  
Wenxi Hu ◽  
Huaying Zhang ◽  
Jingwen Ai ◽  
Zixuan Zheng
Keyword(s):  
Automatic Segmentation ◽  
Field Measurements ◽  
Voltage Sag ◽  
Segmentation Method ◽  
Jump Point ◽  
Singular Value Decomposition Method ◽  
Research Activities ◽  
Power Quality Monitoring ◽  
The Mean ◽  
Value Decomposition

Voltage sag characterization is essential for extracting information about a sag event’s origin and how sag events impact sensitive equipment. In response to such needs, more characteristics are required, such as the phase-angle jump, point-on-wave, unbalance, and sag type. However, the absence of an effective automatic segmentation method is a barrier to obtaining these characteristics. In this paper, an automatic segmentation method is proposed to improve this situation. Firstly, an extended voltage sag characterization method is described, in which segmentation plays an important role. Then, a multi-resolution singular value decomposition method is introduced to detect the boundaries of each segment. Further, the unsolved problem of how to set a threshold adaptively for different waveforms is addressed, in which the sag depth, the mean square error, and the entropy of the sag waveform are considered. Simulation data and field measurements are utilized to validate the effectiveness and reliability of the proposed method. The results show that the accuracies of both boundary detection and segmentation obtained using the proposed method are higher than those obtained using existing methods. In general, the proposed method can be implemented into a power quality monitoring system as a preprocess to support related research activities.

Automatic Segmentation of Pores in Weld Images Based on Transition Region Extraction

2012 ◽  
Vol 217-219 ◽  
pp. 1964-1967
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun ◽  
Peng Liu
Keyword(s):  
Transition Region ◽  
Automatic Segmentation ◽  
Experimental Results ◽  
Gray Level ◽  
Segmentation Method ◽  
Complex Environment ◽  
Weld Defects ◽  
Contrast Enhanced ◽  
Region Extraction ◽  
Low Pass

In allusion to the complex images of weld defects, weak contrast between the target and the background, a new segmentation method based on gray level difference transition region extraction is proposed. The paper analyzes the characteristic of weld defects, and then low-pass filtering and contrast enhanced are used to enhance the clarity. Finally, we extract the transition region and confirm a threshold for defects segmentation. The experimental results show that the method can extract the transition region more accurate, and segment the image much better in complex environment.

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