The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pour améliorer les performances d'alignement non linéaire des modèles d'apparence active (AAM), nous appliquons une variante de l'algorithme d'apprentissage de variété non linéaire, Local Linear Embedded, pour modéliser la variété forme-texture. Les expériences montrent que notre méthode maintient un résidu d'alignement plus faible pour certains mouvements à petite échelle par rapport à l'AAM traditionnelle basée sur l'analyse en composantes principales (ACP) et réussit un alignement sur des mouvements à grande échelle en cas d'échec de l'AAM-PCA.
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Xiaokan WANG, Xia MAO, Catalin-Daniel CALEANU, "Nonlinear Shape-Texture Manifold Learning" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 7, pp. 2016-2019, July 2010, doi: 10.1587/transinf.E93.D.2016.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2016/_p
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@ARTICLE{e93-d_7_2016,
author={Xiaokan WANG, Xia MAO, Catalin-Daniel CALEANU, },
journal={IEICE TRANSACTIONS on Information},
title={Nonlinear Shape-Texture Manifold Learning},
year={2010},
volume={E93-D},
number={7},
pages={2016-2019},
abstract={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.},
keywords={},
doi={10.1587/transinf.E93.D.2016},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Nonlinear Shape-Texture Manifold Learning
T2 - IEICE TRANSACTIONS on Information
SP - 2016
EP - 2019
AU - Xiaokan WANG
AU - Xia MAO
AU - Catalin-Daniel CALEANU
PY - 2010
DO - 10.1587/transinf.E93.D.2016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2010
AB - 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.
ER -