A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model

2001 ◽  
Vol 9 (5) ◽  
pp. 549-557 ◽  
Author(s):  
R. Chengalvarayan ◽  
Li Deng
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Maximum A Posteriori ◽  
A Posteriori

Video Sequences Foreground Enhancement Using Hidden Markov Model

2014 ◽  
Vol 989-994 ◽  
pp. 3872-3876
Author(s):  
Dong Yue Lv ◽  
Dong Yan Liu ◽  
Zhi Pei Huang ◽  
Neng Hai Yu ◽  
Jian Kang Wu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Video Sequence ◽  
Hidden Markov ◽  
Abnormal Behavior ◽  
Foreground Detection ◽  
A Posteriori ◽  
Single Frame ◽  
The Relationship ◽  
Enhancement Algorithm

Foreground detection is an important part in video surveillance system. The detection results will significantly affect the performance of tracking, abnormal behavior analysis and other following procedures. Many algorithms have been proposed to improve the detection performance. However, these algorithms simply focus on one single frame, ignoring the relationship among the detection results of one target in successive frames. This paper presents a novel foreground enhancement algorithm using Hidden Markov Model (HMM). In a video sequence, one target in successive frames usually has similar shape, size, et al. With this property, the target can be modeled by HMM and enhanced using the result of its prior frame. The observation of HMM is obtained by ViBe. The enhancement result is then estimated by using Maximum A Posteriori (MAP). Experimental results show that compared with the state-of-art algorithm, the proposed method can enhance foreground detection effectively.

scholarly journals Towards Describing Visual Explanation using Machine Learning

2020 ◽  
Vol 9 (2) ◽  
pp. 1699-1703
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Category Label ◽  
Model Parameters ◽  
A Posteriori ◽  
Class Prediction ◽  
Systems Research ◽  
Special Places

Current available visible explanation generating systems research to easily absolve a class prediction. Still, they may additionally point out visible parameters attribute which replicate a strong category prior, though the proof may additionally not clearly be in the pic. This is specifically regarding as alternatively such marketers fail in constructing have confidence with human users. We proposed our own version, which makes a speciality of the special places of house of the seen item, together predicts the category label & interprets why the expected label is proper for the image. The machine proposes to annotate the images automatically using the Markov cache model. To annotate images, principles are represented as states through the usage of Hidden Markov model. The model parameters were estimated as part of a set of images and manual annotations. This is a great collection of checks, albeit automatically, with the possibility a posteriori of the concepts presented in her.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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