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Gesture recognition using principal component analysis, multi-scale theory, and hidden Markov models

Hai, Wu (2002) Gesture recognition using principal component analysis, multi-scale theory, and hidden Markov models. PhD thesis, Dublin City University.

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Abstract

In this thesis, a dynamic gesture recognition system is presented which requires no special hardware other than a Web cam . The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical m ulti-scale theory and Discrete Hidden Markov Models (DHMMs). We use a hierarchical decision tree based on multi-scale theory. Firstly we convolve all members of the training data with a Gaussian kernel, w h ich blu rs d iffe ren c e s b e tw e en images and reduces their separation in feature space. Th is reduces the number of eigen vectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space in to several clusters using the £-means algorithm. Then the level of b lurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided in to clusters and the process is repeated. We thus produce a tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures with very little computational cost. The output of the decision tree is then input in to the DHMM recogniser to recognise temporal information.

Item Type:Thesis (PhD)
Date of Award:2002
Refereed:No
Supervisor(s):Sutherland, Alistair
Uncontrolled Keywords:Principal Component Analysis; PCA; hierarchical multi-scale theory; Discrete Hidden Markov Models; DHMMs
Subjects:Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
ID Code:17440
Deposited On:31 Oct 2012 14:55 by Fran Callaghan. Last Modified 31 Oct 2012 14:55

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