Discrete Hidden Markov Modes with Application to Isolated User-Dependent Hand Gesture Recognition.

摘要

The development of computers and the theory of doubly stochastic processes, have led to a wide variety of applications of the hidden Markov models (HMMs). Due to their computational efficiency, discrete HMMs are often favoured. HMMs offer a flexible way of presenting events with temporal and dynamical variations. Both of these matters are present in hand gestures, which are of increasing interest in the research of human-computer interaction (HCI) technologies. The exploitation of human-to-human communication modalities has become actual in HCI applications. It is even expected, that the existing HCI techniques become a bottleneck in the effective utilization of the available information flow. In this work it is given mathematically uniform presentation of the theory of discrete hidden Markov models. Especially, three basic problems, scoring, decoding and estimation, are considered. To solve these problems it is presented forward and backward algorithms, Viterbi algorithm, and Baum-Welch algorithms, respectively.