Expressing Relational and Temporal Knowledge in Visual Probabilistic
  Networks
  Bayesian networks have been used extensively in diagnostic tasks such as
medicine, where they represent the dependency relations between a set of
symptoms and a set of diseases. A criticism of this type of knowledge
representation is that it is restricted to this kind of task, and that it
cannot cope with the knowledge required in other artificial intelligence
applications. For example, in computer vision, we require the ability to model
complex knowledge, including temporal and relational factors. In this paper we
extend Bayesian networks to model relational and temporal knowledge for
high-level vision. These extended networks have a simple structure which
permits us to propagate probability efficiently. We have applied them to the
domain of endoscopy, illustrating how the general modelling principles can be
used in specific cases.
