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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
AAM(Active Appearance Models)의 비선형 정렬 성능을 향상시키기 위해 비선형 다양체 학습 알고리즘인 Local Linear Embedded의 변형을 모델 모양-질감 다양체에 적용합니다. 실험에 따르면 우리의 방법은 주성분 분석(PCA)을 기반으로 한 기존 AAM과 비교하여 일부 소규모 움직임에 대해 더 낮은 정렬 잔차를 유지하고 PCA-AAM이 실패할 때 대규모 동작에 대해 성공적으로 정렬하는 것으로 나타났습니다.
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부
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
부
@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},}
부
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 -