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Active shape models are a powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have beendoi:10.1109/cvpr.2005.265 dblp:conf/cvpr/LangsPB05 fatcat:njaf6y3ihbhxxnk5pyozvtcph4