Nonparametric hierarchical mixture models based on asymmetric Gaussian distribution

2020 ◽  
Vol 106 ◽  
pp. 102829
Author(s):  
Ziyang Song ◽  
Samr Ali ◽  
Nizar Bouguila ◽  
Wentao Fan
Keyword(s):  
Mixture Models ◽  
Gaussian Distribution ◽  
Hierarchical Mixture Models

Two New Mixture Models Related to the Inverse Gaussian Distribution

2008 ◽  
Vol 12 (1) ◽  
pp. 199-212 ◽  
Author(s):  
Samuel Kotz ◽  
Víctor Leiva ◽  
Antonio Sanhueza
Keyword(s):  
Mixture Models ◽  
Gaussian Distribution ◽  
Inverse Gaussian Distribution ◽  
Inverse Gaussian

scholarly journals Hierarchical Mixture Models for Expression Profiles

2009 ◽  
pp. 40-52 ◽  
Author(s):  
Michael A. Newton ◽  
Ping Wang ◽  
Christina Kendziorski ◽  
Marina Vannucci
Keyword(s):  
Mixture Models ◽  
Expression Profiles ◽  
Hierarchical Mixture Models

Hierarchical mixture models for biclustering in microarray data

2011 ◽  
Vol 11 (6) ◽  
pp. 489-505 ◽  
Author(s):  
F Martella ◽  
M Alfò ◽  
M Vichi
Keyword(s):  
Mixture Models ◽  
Microarray Data ◽  
Hierarchical Mixture Models

Video Object Detection Algorithm Based on Gaussian Mixture Models

2013 ◽  
Vol 721 ◽  
pp. 587-590
Author(s):  
Zhao Xia Fu ◽  
Li Ming Wang
Keyword(s):  
Object Detection ◽  
Mixture Models ◽  
Gaussian Distribution ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Moving Object ◽  
Current Frame ◽  
Background Estimation ◽  
Object Based

The moving object detection of the video image is the basis of sequence image analysis, and it is the research hot issue of today’s foreign and domestic scholars. For detecting the moving object from the scene image in time, a detection algorithm of video moving object based on Gaussian mixture models is proposed in the paper. The pixel values are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixel of the current frame determines whether the light has a larger change, and it combines with the frame-difference method to detect moving object. The experimental results show that the algorithm can quickly and accurately establish the background model and accurately segment the foreground object.

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