Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is...
Xiuyao Song, Chris Jermaine, Sanjay Ranka, John Gu...
The usage of Gaussian mixture models for video segmentation has been widely adopted. However, the main difficulty arises in choosing the best model complexity. High complex models ...
This paper presents a Bayesian approach to achieve efficient and accurate motion tracking in monocular image sequences. We first extract a deterministic motion model with six degr...
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...
MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientat...