@article {14,
	title = {Visual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models},
	journal = {Circuits and Systems for Video Technology, IEEE Transactions on},
	volume = {22},
	year = {2012},
	month = {July},
	pages = {1076 -1086},
	keywords = {active learning-based visual workflow recognition, Bayes methods, classification variance, data labeling, hidden Markov models, image sequences, learned model, learning (artificial intelligence), maximum a posteriori training, maximum information gain method, maximum likelihood training, MFHMM parameters, model variance, multistream fused hidden Markov models, overfitting issues, point estimate-based methods, posterior distribution, training methods, unlabeled data, variational Bayesian treatment, video streaming, video surveillance},
	issn = {1051-8215},
	doi = {10.1109/TCSVT.2012.2189795},
	author = {Sotirios P. Chatzis and D. Kosmopoulos}
}
@proceedings {15,
	title = {A robust approach towards sequential data modeling and its application in automatic gesture recognition},
	journal = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {April},
	pages = {1937-1940},
	keywords = {automatic gesture recognition, data representation, finite Gaussian mixture model, Gaussian distribution, gesture recognition, hidden Markov model, hidden Markov models, hidden state distribution, maximum likelihood estimation, maximum likelihood framework, parameter estimation, sequential data modeling, student-t mixture model, time series, time series modeling},
	doi = {10.1109/ICASSP.2008.4518015},
	author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.}
}
