Q-MAF Shape Decomposition

Rasmus Larsen, Hrafnkell Eiriksson, Mikkel Bille Stegmann, Wiro J. Niessen et al. (Editor)

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    Abstract

    This paper address the problems of generating a low dimensional representation of the shape variation present in a set of shapes represented by a number of landmark points. First, we will present alternatives to the featured Least-Squares Procrustes alignment based on the L1-norm and the L-inf-norm. Second, we will define a new shape decomposition based on the Maximum Autocorrelation Factor (MAF) analysis, and investigate and compare its properties to the Principal Components Analysis (PCA). It is shown that Molgedey-Schuster algorithm for Independent Component Analysis (ICA) is equivalent to the MAF analysis. The shape MAF analysis utilises the natural order of landmark points along shape contours.
    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands
    PublisherSpringer
    Publication date2001
    Pages837-844
    Publication statusPublished - 2001

    Cite this

    Larsen, R., Eiriksson, H., Stegmann, M. B., & Niessen et al., W. J. (Ed.) (2001). Q-MAF Shape Decomposition. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands (pp. 837-844). Springer. http://www.imm.dtu.dk/pubdb/p.php?117