Nonlinear Shape-Texture Manifold Learning
Nonlinear Shape-Texture Manifold Learning
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DOI：
10.1587/transinf.e93.d.2016
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Xiaokang Wang;Xia Mao;C. Căleanu
中科院分区：
文献类型：
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作者：
Xiaokang Wang;Xia Mao;C. Căleanu
为了提高主动外观模型（AAM）的非线性对齐性能，我们采用了一种非线性流形学习算法的变体，局部线性嵌入，模型的形状纹理流形。实验结果表明，与传统的基于主成分分析（PCA）的AAM方法相比，该方法对小尺度运动具有较低的配准残差，对大尺度运动也能成功配准。
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.