Gaussian mixture models for on-line signature verification
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In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...
In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...
In this paper, we propose a Markov chain modeling of complicated phenomena observed from coupled chaotic oscillators. Once we obtain the transition probability matrix from computer simulation results, various statistical quantities can be easily calculated ...
This thesis focuses on the analysis of the trajectories of a mobile agent. It presents different techniques to acquire a quantitative measure of the difference between two trajectories or two trajectory datasets. A novel approach is presented here, based o ...
In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps ...
In this paper, a novel statistical generative model to describe a face is presented, and is applied on the face authentication task. Classical generative models used so far in face recognition, such as Gaussian Mixture Models (GMM) and Hidden Markov Models ...
It has been previously demonstrated that systems based on Hidden Markov Models (HMMs) are suitable for face recognition. The proposed approaches in the literature are either HMMs with one-dimensional (1D-HMMs) or two-dimensional (2D-HMMs) topology. Both ha ...
In this paper, a novel statistical generative model to describe a face is presented, and is applied to the face authentication task. Classical generative models used so far in face recognition, such as Gaussian Mixture Models (GMMs) and Hidden Markov Model ...
Multivariate time series are sequences, whose order is provided by a time index; thus, most classifiers used on such data treat time as a special quantity, and encode it, structurally in a model. A typical example of such models is the hidden Markov model, ...
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We propose a deep study on tissue modelization and classification techniques on T1-weighted MR images. Six approaches have been taken into account to perform this validation study. We consider first the Finite Gaussian Mixture model (A-FGMM) and a Bayes cl ...