Nonlinear Shape-Texture Manifold Learning
Xiaokan Wang, Xia Mao, Cătălin Daniel Căleanu · IEICE Transactions on Information and Systems · 2010
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.