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New deep learning approaches to domain adaptation and their applications in 3D hand pose estimation

Abdi, Masoud 2019, New deep learning approaches to domain adaptation and their applications in 3D hand pose estimation, Ph.D. thesis, Institute for Intelligent Systems Research and Innovation, Deakin University.

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Title New deep learning approaches to domain adaptation and their applications in 3D hand pose estimation
Author Abdi, Masoud
Institution Deakin University
School Institute for Intelligent Systems Research and Innovation
Faculty Deputy Vic-Chancellor Research Group
Degree type Research doctorate
Degree name Ph.D.
Thesis advisor Nahavandi SaeidORCID iD for Nahavandi Saeid orcid.org/0000-0002-0360-5270
Date submitted 2019-02
Summary This study investigates several methods for using artificial intelligence to give machines the ability to see. It introduced several methods for image recognition that are more accurate and efficient compared to the existing approaches.
Language eng
Indigenous content off
Field of Research 080104 Computer Vision
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
Description of original 127 p.
Copyright notice ┬ęThe author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30132573

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Created: Tue, 26 Nov 2019, 12:10:09 EST by Penny Mcevoy

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.