Minimum spanning forest with embedded edge inconsistency measurement for color-guided depth map upsampling

Zuo, Y; Wu, Q; Zhang, J; An, P

Minimum spanning forest with embedded edge inconsistency measurement for color-guided depth map upsampling

Zuo, Y Wu, Q

Zhang, J

An, P

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2017, pp. 211 - 216
Issue Date:: 2017-08-28

Closed Access

	Filename	Description	Size
	080193660Yifan-ICME17.pdf	Published version	602.92 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zuo, Y	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	An, P	en_US
dc.date.issued	2017-08-28	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2017, pp. 211 - 216	en_US
dc.identifier.isbn	9781509060672	en_US
dc.identifier.issn	1945-7871	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122181
dc.description.abstract	© 2017 IEEE. Color-guided depth map up-sampling, such as Markov-Random-Field-based (MRF-based) methods, is a popular depth map enhancement solution, which normally assumes edge consistency between color image and corresponding depth map. It calculates the coefficients of smoothness term in MRF according to such assumption. However, such consistency is not always true which leads to texture-copying artifacts and blurring depth edges. In this paper, we propose a novel coefficient computing scheme for smoothness term in MRF which is based on the distance between pixels in the Minimum Spanning Trees (Forest) to better preserve depth edges. The explicit edge inconsistency measurement is embedded into weights of edges in Minimum Spanning Trees, which significantly mitigates texture-copying artifacts. The proposed method is evaluated on Middlebury datasets and ToF-Mark datasets which demonstrates improved results compared with state-of-the-art methods.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo	en_US
dc.relation.isbasedon	10.1109/ICME.2017.8019366	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Minimum spanning forest with embedded edge inconsistency measurement for color-guided depth map upsampling	en_US
dc.type	Conference Proceeding
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	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Color-guided depth map up-sampling, such as Markov-Random-Field-based (MRF-based) methods, is a popular depth map enhancement solution, which normally assumes edge consistency between color image and corresponding depth map. It calculates the coefficients of smoothness term in MRF according to such assumption. However, such consistency is not always true which leads to texture-copying artifacts and blurring depth edges. In this paper, we propose a novel coefficient computing scheme for smoothness term in MRF which is based on the distance between pixels in the Minimum Spanning Trees (Forest) to better preserve depth edges. The explicit edge inconsistency measurement is embedded into weights of edges in Minimum Spanning Trees, which significantly mitigates texture-copying artifacts. The proposed method is evaluated on Middlebury datasets and ToF-Mark datasets which demonstrates improved results compared with state-of-the-art methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122181