scholarly journals Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4316
Author(s):  
Lixiao Mu ◽  
Xiaobing Xu ◽  
Zhanran Xia ◽  
Bin Yang ◽  
Haoran Guo ◽  
...  
Keyword(s):  
Mean Shift ◽  
Electrical Equipment ◽  
Power Cable ◽  
Infrared Images ◽  
Mean Shift Algorithm ◽  
Diagnosis Method ◽  
Manual Selection ◽  
The Mean ◽  
Condition Diagnosis ◽  
Hv Cable

Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

The mean shift algorithm and its relation to kernel regression

2016 ◽  
Vol 348 ◽  
pp. 198-208 ◽  
Author(s):  
Youness Aliyari Ghassabeh ◽  
Frank Rudzicz
Keyword(s):  
Mean Shift ◽  
Kernel Regression ◽  
Mean Shift Algorithm ◽  
The Mean

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

AN AUTONOMOUS SYSTEM FOR GROUND-BASED CLOUD TRACKING USING THE MEAN-SHIFT ALGORITHM

2011 ◽  
Author(s):  
M.D. O’Toole ◽  
S.A. Wormald ◽  
D. Kerr ◽  
J. Coupland ◽  
A.P. Sandford
Keyword(s):  
Autonomous System ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Cloud Tracking

Research on Face Tracking in Community Monitoring System

2014 ◽  
Vol 484-485 ◽  
pp. 358-362
Author(s):  
Shuang Liu
Keyword(s):  
Target Tracking ◽  
Skin Color ◽  
Mean Shift ◽  
Texture Features ◽  
Mean Shift Algorithm ◽  
Local Character ◽  
Community Monitoring ◽  
The Mean ◽  
Target Block ◽  
Time Update

This paper proposes a block Mean-Shift algorithm based on target real-time update and LBP texture features, through the target update improves the accuracy of target tracking, enhances the local character of the target through the target block, so as to improve the robustness of algorithm based on skin color backgrounds. And then analyze the Mean-Shift algorithm cannot recover quickly lost target tracking defects, and its improvement by combining the frame difference method.

scholarly journals Thalamus Segmentation from Diffusion Tensor Magnetic Resonance Imaging

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Ye Duan ◽  
Xiaoling Li ◽  
Yongjian Xi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Mean Shift ◽  
Fixed Number ◽  
Resonance Imaging ◽  
Mean Shift Algorithm ◽  
Nuclei Segmentation ◽  
Segmentation Algorithms ◽  
The Mean

We propose a semi-automatic thalamus and thalamus nuclei segmentation algorithm from Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) based on the mean-shift algorithm. Comparing with existing thalamus segmentation algorithms which are mainly based on K-means algorithm, our mean-shift based algorithm is more flexible and adaptive. It does not assume a Gaussian distribution or a fixed number of clusters. Furthermore, the single parameter in the mean-shift based algorithm supports hierarchical clustering naturally.

scholarly journals Motion Capture and Intelligent Correction Method of Badminton Movement Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yibo Zhang ◽  
Jianjun Tang ◽  
Hui Huang
Keyword(s):  
Machine Vision ◽  
Target Tracking ◽  
Motion Capture ◽  
Mean Shift ◽  
Weak Links ◽  
Bottom Line ◽  
Mean Shift Algorithm ◽  
The Mean ◽  
Combined Algorithm ◽  
Improved Algorithm

In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.

scholarly journals Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1370
Author(s):  
Tariq Ali ◽  
Khalid Masood ◽  
Muhammad Irfan ◽  
Umar Draz ◽  
Arfan Ali Nagra ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Mean Shift ◽  
Sample Entropy ◽  
Tissue Samples ◽  
Mean Shift Algorithm ◽  
Second Stage ◽  
Three Stages ◽  
The Mean ◽  
Cancer Tissues ◽  
Cancerous Cells

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.

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