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Deep representation learning for marker-less human posture analysis

Abobakr, Ahmed 2019, Deep representation learning for marker-less human posture analysis, Ph.D. thesis, Institute for Intelligent Systems Research and Innovation, Deakin University.

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Title Deep representation learning for marker-less human posture analysis
Author Abobakr, AhmedORCID iD for Abobakr, Ahmed orcid.org/0000-0002-6664-2335
Institution Deakin University
School Institute for Intelligent Systems Research and Innovation
Degree type Research doctorate
Degree name Ph.D.
Thesis advisor Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Date submitted 2019-01-04
Summary This thesis presents a holistic human posture analysis system. The proposed system leverages the state-of-the-art deep learning techniques to feature a comprehensive pipeline. Moreover, a new nonlinear computational layer is proposed to the deep convolutional neural network architectures to incorporate human perception capabilities into the deep learning architectures.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
080104 Computer Vision
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Description of original 160 p.
Copyright notice ┬ęThe author
Free to Read? Yes
Use Rights Did not indicate type of CC licence.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30117162

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Created: Wed, 30 Jan 2019, 10:50:30 EST by Bayne Christine

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.