Q-MAF Shape Decomposition

Rasmus Larsen; Hrafnkell Eiriksson; Mikkel Bille Stegmann; Wiro J. Niessen et al.

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 language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands
Publisher	Springer
Publication date	2001
Pages	837-844
Publication status	Published - 2001

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title = "Q-MAF Shape Decomposition",

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.",

author = "Rasmus Larsen and Hrafnkell Eiriksson and Stegmann, {Mikkel Bille} and {Niessen et al.}, {Wiro J.}",

year = "2001",

language = "English",

pages = "837--844",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands",

publisher = "Springer",

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T1 - Q-MAF Shape Decomposition

AU - Larsen, Rasmus

AU - Eiriksson, Hrafnkell

AU - Stegmann, Mikkel Bille

A2 - Niessen et al., Wiro J.

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 837

EP - 844

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, 4th Int. Conference, Utrecht, The Netherlands

PB - Springer

ER -

Q-MAF Shape Decomposition

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