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.

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Author or Creator: 
Mirolli M.
Ferrauto T.
Nolfi S.
Carfax Publishing,, Abingdon (P.O.Box 25, Abingdon, Oxfordshire OX14 3UE) , Regno Unito
Connection science (Print) 22 (2010): 331–354. doi:10.1080/09540091.2010.505976
info:cnr-pdr/source/autori:Mirolli M., Ferrauto T., Nolfi S./titolo:Categorisation through Evidence Accumulation in an Active Vision System/doi:10.1080/09540091.2010.505976/rivista:Connection science (Print)/anno:2010/pagina_da:331/pagina_a:354/intervallo
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Ritratto di Marco Mirolli
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Ritratto di Stefano Nolfi
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