Fast robust detection of edges in noisy depth images

Liu, W; Chen, X; Wu, Q; Yang, J

Fast robust detection of edges in noisy depth images

Liu, W Chen, X Wu, Q

Yang, J

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Publication Type:: Journal Article
Citation:: Journal of Electronic Imaging, 2016, 25 (5)
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Liu, W	en_US
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	Journal of Electronic Imaging, 2016, 25 (5)	en_US
dc.identifier.issn	1017-9909	en_US
dc.identifier.uri	http://hdl.handle.net/10453/100244
dc.description.abstract	© 2016 SPIE and IS & T. Depth edges play an important role in depth image upsampling. Many recent upsampling methods rely on the prior images of depth edges to preserve sharp depth edges in restored depth images. However, recent depth edge detection methods are not robust against the noise in depth images. Some methods are also too time-consuming. We propose a method to efficiently detect edges in depth images. The proposed method is very simple but very robust against the noise in depth images. It is also fast and has near O(1) implementation. We apply the proposed method to the existing edge guided depth image upsampling. Experimental results on both simulated and real data show the effectiveness of the proposed method.	en_US
dc.relation.ispartof	Journal of Electronic Imaging	en_US
dc.relation.isbasedon	10.1117/1.JEI.25.5.053003	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fast robust detection of edges in noisy depth images	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	25	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 2016 SPIE and IS & T. Depth edges play an important role in depth image upsampling. Many recent upsampling methods rely on the prior images of depth edges to preserve sharp depth edges in restored depth images. However, recent depth edge detection methods are not robust against the noise in depth images. Some methods are also too time-consuming. We propose a method to efficiently detect edges in depth images. The proposed method is very simple but very robust against the noise in depth images. It is also fast and has near O(1) implementation. We apply the proposed method to the existing edge guided depth image upsampling. Experimental results on both simulated and real data show the effectiveness of the proposed method.

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http://hdl.handle.net/10453/100244