Q-MAF Shape Decomposition

Rasmus Larsen, Hrafnkell Eiriksson, Mikkel B. Stegmann

AbstractThis 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.
TypeConference paper [With referee]
ConferenceMedical Image Computing and Computer-Assisted Intervention - MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands
EditorsWiro J. Niessen, Max A. Viergever
Year2001    Month October    Vol. 2208    pp. 837-844
PublisherSpringer
SeriesLecture Notes in Computer Science
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics