Temporal structure analysis of broadcast tennis video using hidden Markov models

2003 ◽  
Author(s):  
Ewa Kijak ◽  
Lionel Oisel ◽  
Patrick Gros
Keyword(s):  
Hidden Markov Models ◽  
Structure Analysis ◽  
Markov Models ◽  
Hidden Markov ◽  
Temporal Structure

Structure analysis of soccer video with domain knowledge and hidden Markov models

2004 ◽  
Vol 25 (7) ◽  
pp. 767-775 ◽  
Author(s):  
Lexing Xie ◽  
Peng Xu ◽  
Shih-Fu Chang ◽  
Ajay Divakaran ◽  
Huifang Sun
Keyword(s):  
Hidden Markov Models ◽  
Structure Analysis ◽  
Domain Knowledge ◽  
Markov Models ◽  
Hidden Markov ◽  
Soccer Video

scholarly journals Uncovering temporal structure in hippocampal output patterns

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Kourosh Maboudi ◽  
Etienne Ackermann ◽  
Laurel Watkins de Jong ◽  
Brad E Pfeiffer ◽  
David Foster ◽  
...  
Keyword(s):  
Spatial Memory ◽  
Hidden Markov Models ◽  
Animal Behavior ◽  
Markov Models ◽  
Hidden Markov ◽  
Temporal Structure ◽  
Cell Activity ◽  
Pyramidal Cells ◽  
Place Cell ◽  
Population Burst

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals’ positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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