Affiliation:
1. School of Computer Science, University of Petroleum and Energy Studies, India
Abstract
In this chapter, hidden Markov model (HMM) is used to apply for gesture recognition. The study comprises the design, implementation, and experimentation of a system for making gestures, training HMMs, and identifying gestures with HMMs in order to better understand the behaviour of hidden Markov models (HMMs). One person extends his flattened, vertical palm toward the other, as though to reassure the other that his hands are safe. The other individual smiles and responds in kind. This wave gesture has been associated with friendship from childhood. Human motions can be thought of as a pattern recognition challenge. A computer can deduce the sender's message and reply appropriately if it can detect and recognise a set of gestures. By creating and implementing a gesture detection system based on the semi-continuous hidden Markov model, this chapter aims to bridge the visual communication gap between computers and humans.
Reference13 articles.
1. An inequality and associated maximization technique in statistical estimation of probabilistic functions of markov processes.;L.Baum;Inequalities,1972
2. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
3. Using configuration states for the representation and recog-nition of gesture.;A.Bobick;Proc. Fifth International Conf. on Computer Vision
4. Nonlinear manifold learning for visual speech recognition
5. Learning-based hand sign recognition using SHOSH-LIF-M.;Y.Cui;Proc. Fifth International Conf. on Computer Vision