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Supervised learning for labelling human body with attached props

Haggag, Hussein 2015, Supervised learning for labelling human body with attached props, PhD thesis, Centre for Integelligent Systems Research, Deakin Univeristy.

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Title Supervised learning for labelling human body with attached props
Author Haggag, Hussein
Institution Deakin Univeristy
School Centre for Integelligent Systems Research
Faculty Centre for Integelligent Systems Research
Degree type Research doctorate
Degree name PhD
Date submitted 2015-06
Keyword(s) training data of human postures
body-part classification accuracy
human body tracking and labelling
attached objects
Summary  In this research, a novel method for generating training data of human postures with attached objects is proposed. The results has shown a significant increase in body-part classification accuracy for subjects with props from 60% to 94% using the generated image set
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080103 Computer Graphics
080104 Computer Vision
Socio Economic Objective 940505 Workplace Safety
Description of original xv, 175 pages : illustrations, tables, graphs, coloured
Copyright notice ┬ęThe Author. All Rights Reserved
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079441

Document type: Thesis
Collections: Higher degree theses (full text)
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Created: Wed, 11 Nov 2015, 15:29:34 EST by Kate Percival

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.