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

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

View alignment with dynamically updated affine tracking

We propose a framework for fast view alignment using adaptive affine tracking. We address the issue of modelling both shape and texture information in eigenspace for view alignment. We present an effective bootstrapping process based on colour segmentation and selective attention. We recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate our approach. 1 Introduction A view-based representation assumes that a piecewise linear vector space exists in which each view is represented by a vector [1]. For object recognition in dynamic scenes using view-based representation, frame to frame view alignment is essential. This requires establishing image correspondences in successive frames of a moving object which may undergo both affine and viewpoint transformations [2]. However, to obtain consistent dense image correspondence is both problematic and expensive since chang..

View Alignment with Dynamically Updated Affine Tracking

We propose a framework for fast view alignment using adaptive affine tracking. We address the issue of modelling both shape and texture information in eigenspace for view alignment. We present an effective bootstrapping process based on colour segmentation and selective attention. We recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate our approach.