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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Neural Clustering Of Correspondences For Visual Pose Estimation |
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Authors: |
Tomás H. Maul, Sapiyan Baba |
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Published in:
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(2009).ECMS
2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel
Pedrera. European Council for Modeling and
Simulation. doi:10.7148/2009 ISBN: 978-0-9553018-8-9 23rd
European Conference on Modelling and Simulation, Madrid, June
9-12, 2009 |
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Citation
format: |
Maul, T. H., & Baba, S.
(2009). Neural Clustering Of Correspondences For Visual Pose Estimation. ECMS
2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera (pp.
820-826). European Council for Modeling and Simulation. doi:10.7148/2009-0820-0826 |
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DOI: |
http://dx.doi.org/10.7148/2009-0820-0826 |
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Abstract: |
This paper is concerned with the
problem of visual pose estimation, which entails, for example, the estimation
of object translations. It adopts a correspondence based approach in general,
and in particular, looks into a neural network implementation of the approach.
The objective of the paper is to demonstrate how the approach can be learnt
via the unsupervised clustering of correspondences into clusters representing
different poses. Purely local (i.e. Hebbian)
mechanisms were adopted in order to ensure not only the practical value of
the learning algorithm but also its biological relevance. The results of the
experiments here reported show that the learning strategy adopted allows for
the successful unsupervised clustering of correspondences, even when the
environment puts forth several difficult challenges, such as scarce or
correlated features. |
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