One powerful methodology for maintaining non-Gaussian distributions is based on random sampling techniques. The effectiveness of ``factored sampling'' and ``Markov chain Monte Carlo'' for interpretation of static images is widely accepted. More recently, factored sampling has been combined with learned dynamical models to propagate probability distributions for object position and shape. Progress in several areas is reported here. First a new observational model is described that takes object opacity into account. Secondly, complex shape models to represent combined rigid and nonrigid motion have been developed, together with a new algorithm to decompose rigid from nonrigid. Lastly, more powerful dynamical prior models have been constructed by appending suitable discrete labels to a continuous system state; this may also have applications to gesture recognition.
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