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Comparison of sparse point distribution models

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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.

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Authors and Affiliations

  1. Deformalyze, Diplomvej 373, 2800, Kongens Lyngby, Denmark

    Søren G. H. Erbou & Martin Vester-Christensen

  2. DTU Informatics, Technical University of Denmark, Richard Petersens Plads, 2800, Kongens Lyngby, Denmark

    Søren G. H. Erbou, Martin Vester-Christensen, Rasmus Larsen & Bjarne K. Ersbøll

  3. Danish Meat Research Institute, Maglegårdsvej 2, 4000, Roskilde, Denmark

    Lars B. Christensen

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  1. Søren G. H. Erbou
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  2. Martin Vester-Christensen
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  3. Rasmus Larsen
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  4. Lars B. Christensen
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  5. Bjarne K. Ersbøll
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Correspondence to Søren G. H. Erbou.

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Erbou, S.G.H., Vester-Christensen, M., Larsen, R. et al. Comparison of sparse point distribution models. Machine Vision and Applications 21, 999–1008 (2010). https://doi.org/10.1007/s00138-009-0203-1

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  • DOI: https://doi.org/10.1007/s00138-009-0203-1

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