@ARTICLE\{IMM2009-05049,
    author       = "S. G. H. Erbou and M. Vester-Christensen and R. Larsen and L. B. Christensen and B. K. Ersb{\o}ll",
    title        = "Comparison of sparse point distribution models",
    year         = "2009",
    keywords     = "Image analysis, Point distribution models, Varimax, Sparse {PCA,} Robotic Tools",
    journal      = "Machine Vision and Applications",
    volume       = "",
    editor       = "",
    number       = "",
    publisher    = "Springer",
    note         = "DOI: 10.1007/s00138-009-0203-1",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5049-full.html",
    abstract     = "This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of {3D} surfaces of a section of the pelvic bone obtained from {CT} scans of 33 porcine carcasses. The superior model with respect to sparsity, reconstruction error and interpretability is found to be a varimax rotated model with a threshold applied to small loadings. The models describe the biological variation in the database and are used for developing robotic tools when automating labor-intensive procedures in abattoirs."
}