Research on GMM Background Modeling and its Covariance Estimation

2011 ◽  
Vol 383-390 ◽  
pp. 2327-2333
Author(s):  
Yun Chu Zhang ◽  
Ru Min Zhang ◽  
Shi Jun Song
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Background Modeling ◽  
Covariance Estimation ◽  
Parameters Estimation ◽  
Convergence Problem ◽  
Slow Convergence ◽  
Segmentation Accuracy ◽  
The Stability

This paper analyzes the background modeling mechanism using Gaussian mixture model and the stability /plasticity dilemma in parameters estimation of GMM background model. To solve the slow convergence problem of Gaussian mean and covariance update formula given by Stauffer, a new updating strategy is proposed, which weighs the model adaptability and motion segmentation accuracy. Experiments show that the proposed algorithm improves the accuracy of modal learning and speed of covariance convergence.

scholarly journals Non-Data Aided Rician Parameters Estimation in Temporal Fading Channel With 3 DoFs Gaussian Mixture Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 62198-62209
Author(s):  
Qilong Zhang ◽  
Guobao Lu ◽  
Wuxiong Zhang ◽  
Fei Shen ◽  
Xuewu Dai ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Fading Channel ◽  
Gaussian Mixture ◽  
Parameters Estimation

Research on Background Modeling by Gaussian Mixture Model without Parameter Adjustment

2013 ◽  
Vol 380-384 ◽  
pp. 1394-1397
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Density Estimation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mean Shift ◽  
Gaussian Mixture ◽  
Background Modeling ◽  
Background Model ◽  
Probability Density Estimation ◽  
Mean Shift Algorithm ◽  
Statistical Background

When modeling background model by Gaussian mixture model, there exist the defects that parameters can not be updated adaptively. In this paper, we adopt mean-shift algorithm to overcome these defects. Firstly, this paper introduces the initialized parameters, such as variance, mean, and weights and others, when modeling and then the parameters are constantly adjusted in the subsequent calculations. Then the statistical background model based on probability density estimation is put forward and using mean-shift algorithm updates the parameters adaptively. At last, the algorithm of mixture Gaussian background modeling method based on mean-shift is implemented. The experimental results show that the algorithm can effectively update parameters adaptively and the obtained background model is better.

Accurate Hotspot Segmentation in Thermal Breast Images with Gaussian Mixture Model Superpixels

2021 ◽  
Vol 11 (5) ◽  
pp. 1327-1333
Author(s):  
Mohamed Yacin Sikkandar ◽  
Natteri M. Sudharsan ◽  
Santhanaraj Balakirshnan ◽  
N. B. Prakash ◽  
G. R. Hemalakshmi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Early Stage ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Thermal Images ◽  
Clinical Method ◽  
Cancer Breast ◽  
Segmentation Accuracy

Thermography is a non-invasive and promising clinical method widely being used in screening of breast cancer. Breast thermal images show the heat distribution, depicting the manifestation of cancerous lesions with hot regions. Accurate segmentation of these hotspots is very vital in determining the severity of the lesions, localization and treatment regimen. In this work, color segmentation based on superpixel scheme is proposed to extract the hotspots from breast thermal images. Here, we employ the most recently proposed Gaussian mixture model superpixels, featuring semantically defined boundaries to segment the hotspots with ease. The proposed system tested with a standard dataset exhibits best performance, evaluated with various statistical metrics. It demonstrates very high segmentation accuracy around 99.84%. Visual analyses and statistical comparisons with ground truth images establish the superiority of proposed system over existing methods. Proposed method could increase diagnostic accuracies of breast cancer and increase the abnormality detection in early stage.

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