DocumentCode :
2043336
Title :
Faithful Shape Representation for 2D Gaussian Mixtures
Author :
Boutin, Mireille ; Comer, Mary
Author_Institution :
Purdue Univ., West Lafayette
Volume :
6
fYear :
2007
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
It has been recently discovered that a faithful representation for the shape of some simple distributions can be constructed using invariant statistics [1,2]. In this paper, we consider the more general case of a Gaussian mixture model. We show that the shape of generic Gaussian mixtures can be represented without any loss by the distribution of the distance between two points independently drawn from this mixture. In other words, we show that if their respective distributions of distances are the same, then there exists a rigid transformation mapping one Gaussian mixture onto the other. Our main motivation is the problem of recognizing the shape of an object represented by points given noisy measurements of these points which can be modeled as a Gaussian mixture.
Keywords :
Gaussian processes; image representation; statistical testing; 2D Gaussian mixture; faithful shape representation; image representation; Databases; Distributed computing; Fingerprint recognition; Gaussian noise; Noise shaping; Object recognition; Position measurement; Shape measurement; Statistical distributions; Statistics; Object recognition; invariant statistics; shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1522-4880
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2007.4379598
Filename :
4379598
Link To Document :
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