Generalised Laplacian Eigenmaps for Modelling and Tracking Human Motions
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-32-1364
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2013.2291497
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 1646
- Volume
- 44
- Issue
- -
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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-
- Research group(s)
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- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel dimensionality reduction approach designed to address stylistic variations in multivariate time series. This is significant since retrieval of human poses is essential to many computer vision applications such as gesture analysis, human-computer interfaces and computer animation. Moreover, as the theoretical contribution allows modelling of variations in multivariate time series in general, this research could also be appled to other applications including the analysis of electroencephalography (EEG) and audio signals. This research was part of the ICBISP-2017 keynote presentation: “Compact Representation of Multivariate Sequences using Structural Laplacian Eigenmaps”, http://icbisp2017.events.theiet.org.cn/?nxtId=247790.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -