Comparison of sparse point distribution models

  • Authors:

  • Søren G. H. Erbou;Martin Vester-Christensen;Rasmus Larsen;Lars B. Christensen;Bjarne K. Ersbøll

  • Affiliations:

  • Deformalyze, Diplomvej 373, 2800, Kongens Lyngby, Denmark and Technical University of Denmark, DTU Informatics, Richard Petersens Plads, 2800, Kongens Lyngby, Denmark;Deformalyze, Diplomvej 373, 2800, Kongens Lyngby, Denmark and Technical University of Denmark, DTU Informatics, Richard Petersens Plads, 2800, Kongens Lyngby, Denmark;Technical University of Denmark, DTU Informatics, Richard Petersens Plads, 2800, Kongens Lyngby, Denmark;Danish Meat Research Institute, Maglegårdsvej 2, 4000, Roskilde, Denmark;Technical University of Denmark, DTU Informatics, Richard Petersens Plads, 2800, Kongens Lyngby, Denmark

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2010

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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.