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Abstract. The emergence of affordable depth cameras has enabled significant advances in human segmentation and pose estimation in recent years. While it leads to impressive results in many tasks, the use of infrared cameras have their drawbacks, in particular the fact that they don't work in direct sunlight. One alternative is to use a stereo pair of cameras to produce a disparity space image. In this work, we propose a robust method of using a disparity space image to create a prior for human segmentation. This new prior leads to greatly improved segmentation results; it can be applied to any task where a stereo pair of cameras is available, and segmentation results are desired. As an application, we show how the prior can be inserted into a dual decomposition formulation for stereo, segmentation and human pose estimation.

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The development of embodied conversational agents (ECA) as companions brings several challenges for both affective and conversational dialogue. These include challenges in generating appropriate affective responses, selecting the overall shape of the dialogue, providing prompt system response times, and handling interruptions. We present an implementation of such a companion showing the development of individual modules that attempt to address these challenges. Further, to resolve resulting conflicts, we present encompassing interaction strategies that attempt to balance the competing requirements along with dialogues from our working prototype to illustrate these interaction strategies in operation. Finally, we provide the results of an evaluation of the companion using an evaluation methodology created for conversational dialogue and including analysis using appropriateness annotation.

In recent years Dialogue Acts have become a popular means of modelling the communicative intentions of human and machine utterances in many modern dialogue systems. Many of these systems rely heavily on the availability of dialogue corpora that have been annotated with Dialogue Act labels. The manual annotation of dialogue corpora is both tedious and expensive. Consequently, there is a growing interest in unsupervised systems that are capable of automating the annotation process. This paper investigates the use of a Dirichlet Process Mixture Model as a means of clustering dialogue utterances in an unsupervised manner. These clusters can then be analysed in terms of the possible Dialogue Acts that they might represent. The results presented here are from the application of the Dirichlet Process Mixture Model to the Dihana corpus.