Extraction and Recognition of Periodically Deforming Objects by Continuous, Spatio-temporal Shape Description.
Extraction and Recognition of Periodically Deforming Objects by Continuous, Spatio-temporal Shape Description.
We demonstrate a novel approach to modelling arbitrary temporally-deforming objects using spatio-temporal Fourier descriptors. This is a continuous boundary descriptor, which can handle shapes that vary in a periodic manner (such as a walking subject). As such, we can handle non-rigid, moving shapes that self-occlude. We show how this approach has led to successful shape extraction and description with both laboratory-sourced and real-world data. A consequence of exploiting temporal shape correlation in this approach has led to very good tolerance of noise and other positive performance factors. Further to this, our new approach holds sufficient descriptive power not only for extraction, but also for description purposes, and we have been pleased to note high recognition rates in human gait recognition on a large database.
0-7695-2158-4
Mowbray, S. D.
d87f6b61-a419-4a6f-95ed-f249706d7ae5
Nixon, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
2004
Mowbray, S. D.
d87f6b61-a419-4a6f-95ed-f249706d7ae5
Nixon, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mowbray, S. D. and Nixon, M. S.
(2004)
Extraction and Recognition of Periodically Deforming Objects by Continuous, Spatio-temporal Shape Description.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition., Washington, DC..
27 Jun - 02 Jul 2004.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We demonstrate a novel approach to modelling arbitrary temporally-deforming objects using spatio-temporal Fourier descriptors. This is a continuous boundary descriptor, which can handle shapes that vary in a periodic manner (such as a walking subject). As such, we can handle non-rigid, moving shapes that self-occlude. We show how this approach has led to successful shape extraction and description with both laboratory-sourced and real-world data. A consequence of exploiting temporal shape correlation in this approach has led to very good tolerance of noise and other positive performance factors. Further to this, our new approach holds sufficient descriptive power not only for extraction, but also for description purposes, and we have been pleased to note high recognition rates in human gait recognition on a large database.
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Published date: 2004
Additional Information:
Event Dates: 27th June - 2nd July, 2004
Venue - Dates:
IEEE Computer Society Conference on Computer Vision and Pattern Recognition., Washington, DC., 2004-06-27 - 2004-07-02
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 259261
URI: http://eprints.soton.ac.uk/id/eprint/259261
ISBN: 0-7695-2158-4
PURE UUID: 2dd6400b-8c94-4a0d-9c82-51bc4ad92971
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Date deposited: 16 Apr 2004
Last modified: 15 Mar 2024 02:35
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Contributors
Author:
S. D. Mowbray
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