In statistical pattern recognition, parameters of distributions are usually estimated from training samples. It is well known that shortage of training samples causes estimation errors which reduce recognition accuracy. By studying estimation errors of eigenvalues, various methods of avoiding recognition accuracy reduction have been proposed. However, estimation errors of eigenvectors have not ...

In this work we review the basic principles of stochastic logic and propose its application to probabilistic-based pattern-recognition analysis. The proposed technique is intrinsically a parallel comparison of input data to various pre-stored categories using Bayesian techniques. We design smart pulse-based stochastic-logic blocks to provide an efficient pattern recognition analysis. The propos...

We present a method for characterizing coverbal gestural units intended for human-avatar interaction. We recorded 12 gesture types, using a motion-capture system. We used the markers positions thus obtained to determine the gestural units after stroke segmentation. We complement our linguistic analysis of gestures with an elaboration of our biomechanical hypotheses, our method of segmentation, ...

We present an approach to model articulated human movements and to analyse their behavioural semantics. First, we describe a novel dynamic and behavioural model that uses movements, a sequence of consecutive poses, from motion captured video data to establish priors for both tracking and behavioural analysis. Second, using that model, we show how we can both learn and subsequently recognise hum...

Human behaviours consist different types of motion; we show how they can be disambiguated into their components in a richer way than that currently possible. Studies on optical flow have concentrated motion alone without the higher order components: snap, jerk and acceleration. We are first to acceleration, jerk, snap constituent parts obtained from image sequences, deployed for analysis, espec...