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Automatically building 2D statistical shapes using the topology preservation model GNG

Garcia Rodriguez, Jose and Angelopoulou, Anastassia and Psarrou, Alexandra and Revett, Kenneth (2006) Automatically building 2D statistical shapes using the topology preservation model GNG. In: Narayanan, P.J. and Nayar, Shree K. and Shum, Heung-Yeung, (eds.) Computer Vision ACCV 2006: 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006: Proceedings, Part I. Lecture notes in computer science (3851). Springer, pp. 519-528. ISBN 3540312196

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Official URL: http://dx.doi.org/10.1007/11612032_53

Abstract

Image segmentation is very important in computer based image interpretation and it involves the labeling of the image so that the labels correspond to real world objects. In this study, we utilise a novel approach to automatically segment out the ventricular system from a series of MR brain images and to recover the shape of hand outlines from a series of 2D training images. Automated landmark extraction is accomplished through the use of the self-organising model the growing neural gas (GNG) network which is able to learn and preserve the topological relations of a given set of input patterns without requiring a priori knowledge of the structure of the input space. The GNG based method is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and results are given showing that the proposed method preserves accurate models.

Item Type:Book Section
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:1492
Deposited On:08 May 2006
Last Modified:14 Oct 2009 12:39

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