In this paper, a simplified 2-D second-order Hidden Markov Model (HMM) with tied state mixtures is applied to the face recognition problem. The mixture of the model states is fully-tied across all models for lower complexity. Tying HMM parameters is a well-known solution for the problem of insufficient training data leading to nonrobust estimation. We show that parameter tying in HMM also enhan...

The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and...

Background: Since psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the ps...

These notes explain how to use a hidden Markov model (HMM) approach for analysing possibly highly nonlinear time series data in a state-space formulation. The text introduces the general state-space model and gives an overview of other methods for filtering and smoothing ranging from the simple linear and Gaussian case to the fully general case. A discretization of the state-space is instrument...

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can beHidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov...

This paper presents a new method for human recognition using the cepstral information. The proposed method consists in extracting the Linear Frequency Cepstral Coefficients (LFCC) from each heartbeat in the homomorphic domain. Thus, the Hidden Markov Model (HMM) under Hidden Markov Model Toolkit (HTK) is used for electrocardiogram (ECG) classification. To evaluate the performance of the classif...

In this paper, through different experimental studies it is demonstrated that the time varying glottal excitation component of speech can be exploited for text independent gender recognition studies. Linear prediction (LP) residual is used as a representation of excitation information in speech. The gender-specific information in the excitation of voiced speech is captured using the Hidden Mark...

We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and h...

Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to basic recognition tasks, we introduce a novel pattern recognizer that operates under partially observable conditions. This Single Action Partially Observable Mar...

Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to a basic recognition task, we introduce a novel pattern recognizer that operates under partially observable conditions. This Single Action Partially Observable Ma...