Categorisation through evidence accumulation in an active vision system
In this paper, we present an artificial vision system that is trained with a genetic algorithm for categorising five different kinds of images (letters) of different sizes. The system, which has a limited field of view, can move its eye so as to explore the images visually. The analysis of the system at the end of the training process indicates that correct categorisation is achieved by (1) exploiting sensory-motor coordination so as to experience stimuli that facilitate discrimination, and (2) integrating perceptual and/or motor information over time through a process of accumulation of partially conflicting evidence. We discuss our results with respect to the possible different strategies for categorisation and to the possible roles that action can play in perception.
categorisation active vision neural networks