Hand gesture recognition by Hidden Markov Models

Liu, Nianjun. (2004). Hand gesture recognition by Hidden Markov Models PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland.

       
Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
THE18158.pdf Full text application/pdf 16.45MB 1
Author Liu, Nianjun.
Thesis Title Hand gesture recognition by Hidden Markov Models
School, Centre or Institute School of Information Technology and Electrical Engineering
Institution The University of Queensland
Publication date 2004
Thesis type PhD Thesis
Supervisor Dr Brian Lovell
Dr Peter Kootsookos
Total pages 166
Language eng
Subjects L
280208 Computer Vision
700199 Computer software and services not elsewhere classified
Formatted abstract
The thesis introduces the components of a hand gesture recognition system comprising hand segmentation, feature extraction and gesture recognition by Hidden Markov Models (HMMs). The major research focusses on the applications of HMMs to Gesture Recognition.

Two applications are developed. They are Six gesture variation and the 26 letter input system. Both perform well and achieve high recognition performance.

Each part of the system is described, including hand segmentation, feature computation, tracking methods, active statistical models, Principal Component Analysis, hand edge detection with fingertip landmarks, and vector Quantization for discrete output.

The emphasis is on the analysis of Hidden Markov Models. There are three types of HMM model structures examined — Fully Connected, Left-Right and Left-Right Banded. Two training methods are compared — the traditional Baum-Welch and the more recent Viterbi Path Counting method. The effect of initial model parameters on the system is also investigated, and a direct computation method for the HMM models parameters is explored. Two special shape gestures (Triangle and Square) are used to enhance the understanding of the learning and recognizing process of the Hidden Markov Models.

Finally, some extensions of HMMs from one-dimension to two- dimensions are examined.
Keyword Optical pattern recognition -- Mathematical models
Image processing -- Digital techniques -- Mathematical models
Markov processes

Document type: Thesis
Collection: UQ Theses (RHD) - UQ staff and students only
 
Citation counts: Google Scholar Search Google Scholar
Created: Fri, 24 Aug 2007, 18:25:22 EST