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  Learning features of intermediate complexity for the recognition of biological motion

Sigala, R., Serre, T., Poggio, T., & Giese, M. (2005). Learning features of intermediate complexity for the recognition of biological motion. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrożny (Eds.), Artificial Neural Networks: Biological Inspirations: ICANN 2005 15th International Conference, Warsaw, Poland, September 11-15, 2005 (pp. 241-246). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D43D-F Version Permalink: https://hdl.handle.net/21.11116/0000-0005-0DD7-B
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F11550822_39.pdf (Publisher version)
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 Creators:
Sigala, R1, Author           
Serre , T, Author
Poggio, T1, Author           
Giese, M1, Author           
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1External Organizations, ou_persistent22              

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 Abstract: Humans can recognize biological motion from strongly impoverished stimuli, like point-light displays. Although the neural mechanism underlying this robust perceptual process have not yet been clarified, one possible explanation is that the visual system extracts specific motion features that are suitable for the robust recognition of both normal and degraded stimuli. We present a neural model for biological motion recognition that learns robust mid-level motion features in an unsupervised way using a neurally plausible memory-trace learning rule. Optimal mid-level features were learnt from image motion sequences containing a walker with, or without background motion clutter. After learning of the motion features, the detection performance of the model substantially increases, in particular in presence of clutter. The learned mid-level motion features are characterized by horizontal opponent motion, where this feature type arises more frequently for the training stimuli without motion clutter. The learned features are consistent with recent psychophysical data that indicates that opponent motion might be critical for the detection of point light walkers.

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 Dates: 2005-09
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/11550822_39
BibTex Citekey: 5537
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Title: 15th International Conference on Artificial Neural Networks (ICANN 2005)
Place of Event: Warsaw, Poland
Start-/End Date: 2005-09-11 - 2005-09-15

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Title: Artificial Neural Networks: Biological Inspirations: ICANN 2005 15th International Conference, Warsaw, Poland, September 11-15, 2005
Source Genre: Proceedings
 Creator(s):
Duch, W, Editor
Kacprzyk, J, Editor
Oja, E, Editor
Zadrożny, S, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 241 - 246 Identifier: ISBN: 978-3-540-28752-0

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 3696 Sequence Number: - Start / End Page: - Identifier: -