Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty

Xu, Qiuhui; Yuan, Shenfang; Huang, Tianqiang

doi:10.3390/s21041283

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Models. In the mixed Gauss background model, each pixel model is composed of multiple single Gaussian models [17][18][19]. In order to improve the efficiency of the algorithm, we need to sort the single Gauss model according to the importance and delete the nonbackground model in time.…”

Section: Sorting and Deleting Of Multiple Single Gaussianmentioning

confidence: 99%

Theory and Method of Data Collection for Mixed Traffic Flow Based on Image Processing Technology

Yao

Xiang

et al. 2021

Mathematical Problems in Engineering

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As a key element of ITS (intelligent traffic systems), traffic information collection facilities play a key role, with ITS being able to analyze the state of mixed traffic more appropriately and can provide effective technical support for the design, management, and the evaluation of constructions. Traffic Infrastructure. Focusing on image processing technology, this study takes pedestrians, electric motor, and vehicles in mixed traffic flow as the research object, and Gaussian mixed model, Kalman filtering, and Fisher linear discriminant are introduced in the recognition system. On this basis, the mixed motion flow data acquisition framework model is elaborated in detail, which includes attribute extraction, object recognition, and object tracking. Given the difficulty in capturing reliable images of objects in real traffic scenes, this study adopted a novel background and foreground classification method with region proposal network so as to decrease the number of regions proposal from 2000 to 300, which can detect objects fast and accurately. Experiments demonstrate that the designed programme can collect the flow data by detecting and tracking moving object in the surveillance video for mixed traffic. Further integration of various modules to achieve integrated collection is another important task for further research and development. In the future, research on dynamic calibration of monocular vision will be carried out for distance measurement and speed measurement of vehicles and pedestrians.

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Section: Sorting and Deleting Of Multiple Single Gaussianmentioning

confidence: 99%

Theory and Method of Data Collection for Mixed Traffic Flow Based on Image Processing Technology

Yao

Xiang

et al. 2021

Mathematical Problems in Engineering

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“…e number of Gaussian models can be estimated using Akaike information criterion and Bayes information criterion, or can be selected based on experience; that is, when the number of samples contained in a typical pose category is small (less than 20 samples), the value of Z is general choose 1 to 2; otherwise, when the number of samples is too small, the estimation of the mean and variance in the sub-Gaussian model will be out of generality [33,34].…”

Section: Motion Information Capture Algorithm Based On Gaussian Mixtu...mentioning

confidence: 99%

Design of Athlete’s Running Information Capture System in Space-Time Domain Based on Virtual Reality

Zhu

2022

Scientific Programming

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In order to improve the training effect of athletes, aiming at the problems of inaccurate information capture results, poor real-time performance of sports information capture, and inability to effectively suppress noise interference in traditional methods, an athlete space-time running information capture system based on virtual reality is designed. Establish the athlete’s human skeleton, obtain the athlete’s sports joint points, design the overall architecture of the athlete’s space-time running information capture system, and realize the whole link design of sports information capture through RF chip, infrared camera, sports data acquisition module, data transmission module, and human-computer interaction module. Based on virtual reality technology, a virtual reality environment is built to obtain the characteristic parameters of athletes’ sports posture in space-time domain, and the median filtering algorithm is used to filter the original signal to eliminate the impact of noise signal on athletes’ sports information capture. Finally, the activity of the motion region is detected, and the motion information is captured combined with the Gaussian mixture model. The experimental results show that the system designed in this paper has high accuracy and anti-interference and can realize the real-time capture of motion information.

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“…The selection of the initial parameters of the EM algorithm is of particular importance for the clustering results, as each new combination of parameters can steer the cluster in a different direction. Random parameter selection is one of the most commonly used solutions for parameter initialization [23,24]. Random selection of initial parameters is a reasonably simple solution, as it is easy to implement.…”

Section: Modified Inversion Density Clustering Algorithmmentioning

confidence: 99%

A New Clustering Method Based on the Inversion Formula

Lukauskas

Ruzgas

2022

Mathematics

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Data clustering is one area of data mining that falls into the data mining class of unsupervised learning. Cluster analysis divides data into different classes by discovering the internal structure of data set objects and their relationship. This paper presented a new density clustering method based on the modified inversion formula density estimation. This new method should allow one to improve the performance and robustness of the k-means, Gaussian mixture model, and other methods. The primary process of the proposed clustering algorithm consists of three main steps. Firstly, we initialized parameters and generated a T matrix. Secondly, we estimated the densities of each point and cluster. Third, we updated mean, sigma, and phi matrices. The new method based on the inversion formula works quite well with different datasets compared with K-means, Gaussian Mixture Model, and Bayesian Gaussian Mixture model. On the other hand, new methods have limitations because this one method in the current state cannot work with higher-dimensional data (d > 15). This will be solved in the future versions of the model, detailed further in future work. Additionally, based on the results, we can see that the MIDEv2 method works the best with generated data with outliers in all datasets (0.5%, 1%, 2%, 4% outliers). The interesting point is that a new method based on the inversion formula can cluster the data even if data do not have outliers; one of the most popular, for example, is the Iris dataset.

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Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty

Cited by 8 publications

References 30 publications

Theory and Method of Data Collection for Mixed Traffic Flow Based on Image Processing Technology

Theory and Method of Data Collection for Mixed Traffic Flow Based on Image Processing Technology

Design of Athlete’s Running Information Capture System in Space-Time Domain Based on Virtual Reality

A New Clustering Method Based on the Inversion Formula

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