Gabor texture in active appearance models

Gao, X; Su, Y; Li, X; Tao, D

Gabor texture in active appearance models

Gao, X Su, Y Li, X Tao, D

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2009, 72 (13-15), pp. 3174 - 3181
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Su, Y	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Neurocomputing, 2009, 72 (13-15), pp. 3174 - 3181	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15181
dc.description.abstract	In computer vision applications, Active Appearance Models (AAMs) is usually used to model the shape and the gray-level appearance of an object of interest using statistical methods, such as PCA. However, intensity values used in standard AAMs cannot provide enough information for image alignment. In this paper, we firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor, three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments, we show that the proposed Gabor representations lead to more accurate and robust matching between model and images. © 2009 Elsevier B.V.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2009.03.003	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Gabor texture in active appearance models	en_US
dc.type	Journal Article
utslib.citation.volume	13-15	en_US
utslib.citation.volume	72	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	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
utslib.copyright.status	closed_access
pubs.issue	13-15	en_US
pubs.publication-status	Published	en_US
pubs.volume	72	en_US

Abstract:

In computer vision applications, Active Appearance Models (AAMs) is usually used to model the shape and the gray-level appearance of an object of interest using statistical methods, such as PCA. However, intensity values used in standard AAMs cannot provide enough information for image alignment. In this paper, we firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor, three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments, we show that the proposed Gabor representations lead to more accurate and robust matching between model and images. © 2009 Elsevier B.V.

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http://hdl.handle.net/10453/15181