Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a set of different object temporal events in the scene for a coherent and robust scene-level behaviour interpretation. In particular, we develop a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) to interpret group activities involving multiple objects captured in an outdoor scene. The model...

Hidden Markov Model (HMM) is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively...

This paper describes a hidden Markov model (HMM) based visual synthesizer designed to assist persons with impairedhearing. This synthesizer builds on results in the area of audio-visual speech recognition. We describe how a correlation HMM can be used to integrate independent acoustic and visual HMMs for speech-to-visual synthesis. Our results show that an HMM correlating model can signi cantly...

In this paper, we presents a comparison between Hidden Markov Model (HMM) and an approach using a hybrid of Vector Quantization (VQ) with HMM methods. The aim of combination scheme used is to improve the standalone HMM performance. A Malay spoken digit database is used for the testing and validation modules. It is shown that, in clean environments, a total success rate (TSR) of 99.97% is achiev...

We present a learning strategy for Hidden Markov Models that may be used to cluster handwriting sequences or to learn a character model by identifying its main writing styles. Our approach aims at learning both the structure and parameters of a Hidden Markov Model (HMM) from the data. A byproduct of this learning strategy is the ability to cluster signals and identify allograph. We provide expe...

In this paper, we propose a new hidden Markov model (HMM) for the space-time evolution of daily rainfall. The hidden Markov chain represents the different meteorological regimes (“weather types”) and it is assumed that this variable explains the dynamics of the precipitation. The spatial structure within hidden weather types is modelled by censored power-transformed Gaussian distributions. It p...

Speech Recognition is a process of transcribing speech to text. Phoneme based modeling is used where in each phoneme is represented by Continuous Density Hidden Markov Model. Mel Frequency Cepstral Coefficients (MFCC) are extracted from speech signal, delta and double-delta features representing the temporal rate of change of features are added which considerably improves the recognition accura...

A new Face Recognition (FR) system based on Singular Values Decomposition (SVD) and pseudo 2D Hidden Markov Model (P2D-HMM) is proposed in this paper. The state sequence of the pseudo 2D HMM are modeled independently which gives superior results when compared to regular 2D HMMs. As a novel point presented here, we have maintained a limited number of quantized Singular Values Decomposition (SVD)...

This paper presents a modified hidden Markov model (HMM) filtering algorithm for detecting multiple dim targets in image sequence under low SNR condition. The proposed algorithm consists of three steps. As a first step, morphological filtering is applied for extracting features in pre-processing level. The second step is a hidden Markov model filter. To enhance a detecting performance of the fi...