A review of active appearance models

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

A review of active appearance models

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

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2010, 40 (2), pp. 145 - 158
Issue Date:: 2010-03-01

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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	2010-03-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2010, 40 (2), pp. 145 - 158	en_US
dc.identifier.issn	1094-6977	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15195
dc.description.abstract	Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research. © 2009 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews	en_US
dc.relation.isbasedon	10.1109/TSMCC.2009.2035631	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A review of active appearance models	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	40	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
dc.location.activity	Paris, France
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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	40	en_US

Abstract:

Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research. © 2009 IEEE.

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http://hdl.handle.net/10453/15195