Nonlinear Shape-Texture Manifold Learning

Nonlinear Shape-Texture Manifold Learning

For improving the nonlinear alignment performance of Active Appearance Models (AAM), we apply a variant of the nonlinear manifold learning algorithm, Local Linear Embedded, to model shape-texture manifold. Experiments show that our method maintains a lower alignment residual to some small scale move...

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Veröffentlicht in:IEICE Transactions on Information and Systems 2010/07/01, Vol.E93.D(7), pp.2016-2019
Hauptverfasser: WANG, Xiaokan, MAO, Xia, CALEANU, Catalin-Daniel
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Alignment
Learning
local linear embedded
manifold learning
Manifolds
Movements
Nonlinearity
Principal component analysis
shape-texture manifold
Small scale
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Beschreibung
Zusammenfassung:For improving the nonlinear alignment performance of Active Appearance Models (AAM), we apply a variant of the nonlinear manifold learning algorithm, Local Linear Embedded, to model shape-texture manifold. Experiments show that our method maintains a lower alignment residual to some small scale movements compared with traditional AAM based on Principal Component Analysis (PCA) and makes a successful alignment to large scale motions when PCA-AAM failed.
ISSN:0916-8532
1745-1361
1745-1361
DOI:10.1587/transinf.E93.D.2016