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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.

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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
Authors :
Ellis, L
Bowden, R
Date : 2007
Contributors :
ContributionNameEmailORCID Computer Science Group, Bielefeld University, Germany,
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:
Depositing User : Symplectic Elements
Date Deposited : 21 May 2012 11:54
Last Modified : 31 Oct 2017 14:34

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