Gabor texture in active appearance models

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dc.contributor.author Gao, Xinbo en_US
dc.contributor.author Su, Ya en_US
dc.contributor.author Li, Xuelong en_US
dc.contributor.author Tao, Dacheng en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T10:00:20Z
dc.date.available 2012-02-02T10:00:20Z
dc.date.issued 2009 en_US
dc.identifier 2011000222 en_US
dc.identifier.citation Gao Xinbo et al. 2009, 'Gabor texture in active appearance models', Elsevier Science Bv, vol. 72, no. 13-15, pp. 3174-3181. en_US
dc.identifier.issn 0925-2312 en_US
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. en_US
dc.language en_US
dc.publisher Elsevier Science Bv en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.neucom.2009.03.003 en_US
dc.title Gabor texture in active appearance models en_US
dc.parent Neurocomputing en_US
dc.journal.volume 72 en_US
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 080100 en_US
dc.personcode 0000066516 en_US
dc.personcode 0000072406 en_US
dc.personcode 0000066518 en_US
dc.personcode 111502 en_US
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.staffid 111502 en_US


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