Nonlinear Shape-Texture Manifold Learning
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.7 pp.2016-2019
- Publication Date
- 2010/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.2016
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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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 -
English
