Learning Responses to Visual Stimuli: A Generic Approach
Ellis, L and Bowden, R (2007) Learning Responses to Visual Stimuli: A Generic Approach In: ICVS 2007, 2007-03-21 - 2007-03-24, Bielefeld, Germany.
Available under License : See the attached licence file.
A general framework for learning to respond appropriately to visual stimulus is presented. By hierarchically clustering percept-action exemplars in the action space, contextually important features and relationships in the perceptual input space are identified and associated with response models of varying generality. Searching the hierarchy for a set of best matching percept models yields a set of action models with likelihoods. By posing the problem as one of cost surface optimisation in a probabilistic framework, a particle filter inspired forward exploration algorithm is employed to select actions from multiple hypotheses that move the system toward a goal state and to escape from local minima. The system is quantitatively and qualitatively evaluated in both a simulated shape sorter puzzle and a real-world autonomous navigation domain.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Related URLs :|
|Additional Information :||Published in 2007 by Applied Computer Science Group, Bielefeld University, Germany, ISBN 978-3-00-020933-8 This document and other contributions archived and available at: http://biecoll.ub.uni-bielefeld.de|
|Depositing User :||Symplectic Elements|
|Date Deposited :||21 May 2012 11:54|
|Last Modified :||09 Jun 2014 13:18|
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