Gabor texture in active appearance models

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dc.contributor.author Gao, X
dc.contributor.author Su, Y
dc.contributor.author Li, X
dc.contributor.author Tao, D
dc.date.accessioned 2012-02-02T10:00:20Z
dc.date.issued 2009-01
dc.identifier.citation Neurocomputing, 2009, 72 (13-15), pp. 3174 - 3181
dc.identifier.issn 0925-2312
dc.identifier.other C1UNSUBMIT 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.
dc.publisher Elsevier Science Bv
dc.relation.isbasedon 10.1016/j.neucom.2009.03.003
dc.title Gabor texture in active appearance models
dc.type Journal Article
dc.parent Neurocomputing
dc.journal.volume 13-15
dc.journal.volume 72
dc.journal.number 13-15 en_US
dc.publocation Amsterdam en_US
dc.identifier.startpage 3174 en_US
dc.identifier.endpage 3181 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 111502
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Computer vision; Active appearance models (AAMs); Gabor; Texture representation en_US
dc.description.keywords Planning, Urban, Local Governance, Ghana, Afric
dc.description.keywords Computer vision
dc.description.keywords Active appearance models (AAMs)
dc.description.keywords Gabor
dc.description.keywords Texture representation
pubs.embargo.period Not known
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/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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