Humans have the fascinating capacity to understand, and anticipate the actions of other humans in the same space, without requiring verbal communication. This ”intention reading” capacity is underpinned by a common motor-repertoire that is shared by all humans, and afforded by a subtle coordination of eye-head-arm movements that encodes the cues and signals that will ultimately be deciphered by...

Colour cues were used to obtain robust detection and tracking of people in relatively unconstrained dynamic scenes. Gaussian mixture models were used to estimate probability densities of colour for skin, clothing and background. These models were used to detect, track and segment people, faces and hands. A technique for dynamically updating the models to accommodate changes in apparent colour d...

This paper presents a two-step method for eliminating the shadows of moving targets. As the first step, we improve the Gaussian mixture model for obtaining a real-time background and a moving foreground; secondly, we figure out the intersection of background difference and moving foreground for eliminating the concomitant shadow [1-5]. Experiment results indicate that our method has a preferabl...

One point of debate in the recent controversy in the media and among policy analysts over the academic achievement of charter school students is whether the charter students are in some way harder to educate than their counterparts enrolled in traditional public schools. In this paper we examine this question using data from the 2002-3 school year in Washington, D.C. We begin by examining a sim...

The incorporation of the Random Vector Functional Link (RVFL) concept into mixture models for predicting conditional probability densities achieves a considerable speed-up of the training process. This allows the creation of a large ensemble of predictors, which results in an improvement in the generalization performance .

We present a Gaussian Mixture model for detecting different types of figurative language in context. We show that this model performs well when the parameters are estimated in an unsupervised fashion using EM. Performance can be improved further by estimating the parameters from a small annotated data set.

The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data come in. However, the behavior of the model is sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G0. Most previous applications have either fixed G0 as a member of a parametric family...