Abstract
Performance on visual tasks such as classification can be enhanced by employing active vision systems. Such systems do not passively receive observations, but have to some extent control over the observations they perceive. There are two general approaches to active vision. The first approach to active vision is a probabilistic approach, in which reducing uncertainty on a part of the world state is the central goal. This uncertainty is modelled by a belief state. The second approach to active vision is a behavioural approach, in which successful behaviour is the central goal. For both approaches, there have been considerable research efforts into designing and studying various active vision models. However, it is not clear how the different existing active vision models relate to each other, and what their relative advantages are. In this report, we identify three main types of active vision models in the probabilistic approach and describe them in a common formal framework.
