Abstract
The desire to produce artificial vision systems which behave in an intelligent, humanlike way or which can autonomously and automatically perform tasks currently only performed by humans has been a goal of Artificial Intelligence research for many decades. Until recently much of the research concentrated on extracting visual representations of objects from single, static scenes. The last decade has seen an increase in interest concerning mobile robotics for navigation, planning and autonomous control as well as for the interpretation of events in real, dynamic scenes. Presented in this thesis is research on artificial vision systems from two different, but both necessary, standpoints. One concerns low-level vision-based behaviour of object tracking based upon a naturalistic theory of perception and behaviour within living systems. The other takes a more application and engineering based approach and its goal is to address high-level scene interpretation and control of processing resources. Numerous experiments are presented to demonstrate the various issues. The two main experiments, corresponding to the two research streams, are a system which is able to fixate complex multi-coloured objects and a fully integrated vision system for predicting and following, with a mobile sensor, events in a dynamic scene.