A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A novel HMM-based clustering algorithm for the analysis of gene expression time-course data
Computational Statistics & Data Analysis
Pattern Recognition Letters
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In this paper, we present an example of operator discriminant factor analysis applied to the study of movement. Our goal is to contribute to the automatic recognition of typical movements and give analytic elements to discuss the differences between the various types. As a signature for the movements, we select the 'Relational Covariance Operator' and explain our motivation. Discriminant analysis cannot be directly applied to covariance operators; so, in order to deal with a well conditioned problem and avoid useless heavy computation, we proceed in two steps. We first achieve a variant of principal components analysis on covariance operators by using convenient metrics, and then apply plain linear discriminant analysis on the resulting axis. Such a method allows a simple and powerful interpretation of the movement typology. We also present an experiment on motion-captured movements performed by dancers from the 'Ballet Atlantique Régine Chopinot'.