Independent component analysis in statistical shape models

@inproceedings{zmc2003IndependentCA,
  title={Independent component analysis in statistical shape models},
  author={Mehmet {\"U}z{\"u}mc{\"u} and Alejandro F. Frangi and Johan H. C. Reiber and Boudewijn P. F. Lelieveldt},
  booktitle={Medical Imaging: Image Processing},
  year={2003}
}
Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of example shapes. However, PCA assumes a number of restrictions on the data that do not always hold. In this paper we explore the use of an alternative shape decomposition, Independent Component Analysis (ICA… CONTINUE READING