A hidden markov model framework for traffic event detection using video features

2005 ◽  
Author(s):  
Xiaokun Li ◽  
F.M. Porikli
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Hidden Markov ◽  
Model Framework

scholarly journals Bayesian hidden Markov models for delineating the pathology of Alzheimer’s disease

2017 ◽  
Vol 28 (7) ◽  
pp. 2112-2124 ◽  
Author(s):  
Kai Kang ◽  
Jingheng Cai ◽  
Xinyuan Song ◽  
Hongtu Zhu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Markov Model ◽  
Cognitive Decline ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Semiparametric Regression ◽  
Model Framework

Alzheimer’s disease is a firmly incurable and progressive disease. The pathology of Alzheimer’s disease usually evolves from cognitive normal, to mild cognitive impairment, to Alzheimer’s disease. The aim of this paper is to develop a Bayesian hidden Markov model to characterize disease pathology, identify hidden states corresponding to the diagnosed stages of cognitive decline, and examine the dynamic changes of potential risk factors associated with the cognitive normal–mild cognitive impairment–Alzheimer’s disease transition. The hidden Markov model framework consists of two major components. The first one is a state-dependent semiparametric regression for delineating the complex associations between clinical outcomes of interest and a set of prognostic biomarkers across neurodegenerative states. The second one is a parametric transition model, while accounting for potential covariate effects on the cross-state transition. The inter-individual and inter-process differences are taken into account via correlated random effects in both components. Based on the Alzheimer’s Disease Neuroimaging Initiative data set, we are able to identify four states of Alzheimer’s disease pathology, corresponding to common diagnosed cognitive decline stages, including cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer’s disease and examine the effects of hippocampus, age, gender, and APOE-[Formula: see text] on degeneration of cognitive function across the four cognitive states.

scholarly journals A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge

2006 ◽  
Vol 22 (24) ◽  
pp. 3016-3024 ◽  
Author(s):  
Jiang Du ◽  
Joel S. Rozowsky ◽  
Jan O. Korbel ◽  
Zhengdong D. Zhang ◽  
Thomas E. Royce ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Tiling Array ◽  
Biological Knowledge ◽  
Model Framework ◽  
Array Data ◽  
Chip Chip

scholarly journals Modeling Temporal Pattern and Event Detection using Hidden Markov Model with Application to a Sludge Bulking Data

2012 ◽  
Vol 12 ◽  
pp. 218-223 ◽  
Author(s):  
Naveen K. Bansal ◽  
Xin Feng ◽  
Wenjing Zhang ◽  
Wutao Wei ◽  
Yuanhao Zhao
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Temporal Pattern ◽  
Hidden Markov ◽  
Sludge Bulking

Hidden Markov model framework for industrial maintenance activities

2014 ◽  
Vol 228 (3) ◽  
pp. 230-242 ◽  
Author(s):  
Bernard Roblès ◽  
Manuel Avila ◽  
Florent Duculty ◽  
Pascal Vrignat ◽  
Stephane Bégot ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model Framework ◽  
Industrial Maintenance ◽  
Maintenance Activities

scholarly journals Bayesian event detection for sport games with hidden Markov model

2011 ◽  
Vol 15 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Shigeru Motoi ◽  
Toshie Misu ◽  
Yohei Nakada ◽  
Tomohiro Yazaki ◽  
Go Kobayashi ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Event Detection ◽  
Hidden Markov
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