Abstract. In this paper we present a system for human body model acquisition and tracking of its parameters from voxel data. 3D voxel reconstruction of the body in each frame is computed from silhouettes extracted from multiple cameras. The system performs automatic model acquisition using a template based initialization procedure and a Bayesian network for refinement of body part size estimates. The twist-based human body model leads to a simple formulation of the extended Kalman filter that performs the tracking and with joint angle limits guarantees physically valid posture estimates. Evaluation of the approach was performed on several sequences with different types of motion captured with six cameras.

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We present a framework for articulated body

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Methodology for sensory profiling of fermented milks has been studied. A vocabulary of sensory attributes has been developed which encompasses most of the difference between samples. Principal Component Analysis, incorporating factor rotation, allowed simplification of the variability between samples to five Principal Components capable of clear interpretation. Sensory mapping was found to be a useful tool for categorizing fermented milks. The acceptability of the fermented milks was successfully modelled, by Partial Least Squares Regression, in terms of a limited number of key attributes. The model explained 88.4% of the variance. The relations between sensory attributes and the composition of the fermented milks were considered using Multiple Linear Regression. Although a number of statistically significant relations were derived they were of poor to modest value for purposes of prediction.