QMAF Shape Decomposition 
Rasmus Larsen, Hrafnkell Eiriksson, Mikkel B. Stegmann

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 LeastSquares Procrustes alignment based on the L1norm and the Linfnorm. 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 MolgedeySchuster 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. 
Type  Conference paper [With referee] 
Conference  Medical Image Computing and ComputerAssisted Intervention  MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands 
Editors  Wiro J. Niessen, Max A. Viergever 
Year  2001 Month October Vol. 2208 pp. 837844 
Publisher  Springer 
Series  Lecture Notes in Computer Science 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Image Analysis & Computer Graphics 