We measure the influence of individual observations on the sequence of the hidden states of the Hidden Markov Model (HMM) by means of the Kullback-Leibler distance (KLD). Namely, we consider the KLD between the conditional distribution of the hidden states’ chain given the complete sequence of observations and the conditional distribution of the hidden chain given all the observations but the o...

In this paper, a decision model of fusion classification based on HMM-DS is proposed, and the training and recognition methods of the model are given. As the pure HMM classifier can’t have an ideal balance between each model with a strong ability to identify its target and the maximum difference between models. So in this paper, the results of HMM are integrated into the DS framework, and HMM p...

A new gait recognition algorithm, the layered time series model (LTSM), is proposed. LTSM is a two-level model which combines the dynamic texture model (DTM) and the hidden Markov model (HMM). A gait cycle is divided into several temporally adjacent clusters and gait features of each cluster are modelled by the DTM. The HMM is built to describe the relationship among the DTMs, which are regarde...

The work presented in this paper describes a Hidden Markov Model (HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the states of the HMM are obtained using the coeecients of the Karhunen-Loeve Transform (KLT). The face recognition method presented in this paper reduces signiicantly the computational complexity of previous HMM-based face ...

We propose an information theoretic framework for quantitative assessment of acoustic modeling for hidden Markov model (HMM) based automatic speech recognition (ASR). Acoustic modeling yields the probabilities of HMM sub-word states for a short temporal window of speech acoustic features. We cast ASR as a communication channel where the input sub-word probabilities convey the information about ...

The performance of multiple hypothesis testing is known to be affected by the statistical dependence among random variables involved. The mechanisms responsible for this, however, are not well understood. We study the effects of the dependence structure of a finite state hidden Markov model (HMM) on the likelihood ratios critical for optimal multiple testing on the hidden states. Various conver...

Node Localization in Wireless Sensor Networks (WSNs) is widely used in many applications. Localization uses particle filter that provides higher network traffic due to continuous updates, which leads to high power consumption. The article presents a range-based localization for Mobile Nodes (MN) that builds up on Hidden Markov Model (HMM) algorithm. The proposed work is based on MN and the stat...

gait contains important information about the status of the human body and physiological signs. in many medical applications, it isimportant to monitor and accurately analyze the gait of the patient. since walking shows the reproducibility signs in several phases,separating these phases can be used for the gait analysis. in this study, a method based on image processing for extracting phases of...

A. Definition A hidden Markov model is a tool for representing probability distributions over sequences of observations [1]. In this model, an observation Xt at time t is produced by a stochastic process, but the state Zt of this process cannot be directly observed, i.e. it is hidden [2]. This hidden process is assumed to satisfy the Markov property, where state Zt at time t depends only on the...