Surface geodesic pattern for 3D deformable texture matching

Hajati, F; Cheraghian, A; Gheisari, S; Gao, Y; Mian, AS

Surface geodesic pattern for 3D deformable texture matching

Hajati, F Cheraghian, A Gheisari, S Gao, Y Mian, AS

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Publication Type:: Journal Article
Citation:: Pattern Recognition, 2017, 62 pp. 21 - 32
Issue Date:: 2017-02-01

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Field	Value	Language
dc.contributor.author	Hajati, F	en_US
dc.contributor.author	Cheraghian, A	en_US
dc.contributor.author	Gheisari, S	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Mian, AS	en_US
dc.date.issued	2017-02-01	en_US
dc.identifier.citation	Pattern Recognition, 2017, 62 pp. 21 - 32	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126948
dc.description.abstract	© 2016 Elsevier Ltd This paper presents a Surface Geodesic Pattern (SGP) representation for matching textured 3D deformable surfaces. SGP encodes the local variations of the surface texture derivatives to extract local information from distinctive textural relationships contained in a geodesic neighborhood. Thus, SGP derives its strength from the fusion of surface texture and shape information at the data level in a way that is invariant to non-rigid deformations. We also propose Gabor Topography Wavelet (GTW) for direct feature extraction from the range data. Both features are combined using a multi-view sparse representation to achieve higher discrimination capability while matching non-rigid 3D surfaces. The performance of the proposed method is evaluated extensively on the Bosphorus face database, the FRGC v2 face database, and the PolyU contact-free hand database and the results are compared to state-of-the-art methods. Experimental results show the effectiveness and superiority of the proposed method in recognizing objects under non-rigid surface deformations.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2016.08.019	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Surface geodesic pattern for 3D deformable texture matching	en_US
dc.type	Journal Article
utslib.citation.volume	62	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Graduate School of Health
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	62	en_US

Abstract:

© 2016 Elsevier Ltd This paper presents a Surface Geodesic Pattern (SGP) representation for matching textured 3D deformable surfaces. SGP encodes the local variations of the surface texture derivatives to extract local information from distinctive textural relationships contained in a geodesic neighborhood. Thus, SGP derives its strength from the fusion of surface texture and shape information at the data level in a way that is invariant to non-rigid deformations. We also propose Gabor Topography Wavelet (GTW) for direct feature extraction from the range data. Both features are combined using a multi-view sparse representation to achieve higher discrimination capability while matching non-rigid 3D surfaces. The performance of the proposed method is evaluated extensively on the Bosphorus face database, the FRGC v2 face database, and the PolyU contact-free hand database and the results are compared to state-of-the-art methods. Experimental results show the effectiveness and superiority of the proposed method in recognizing objects under non-rigid surface deformations.

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http://hdl.handle.net/10453/126948