Object segmentation using growing neural gas and generalized gradient vector flow in the geometric algebra framework

Authors:
Jorge Rivera-Rovelo;Silena Herold;Eduardo Bayro-Corrochano
Affiliations:
CINVESTAV Unidad Guadalajara, Jalisco, México;Universidad de Oriente, Cuba;CINVESTAV Unidad Guadalajara, Jalisco, México
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

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Abstract

In this paper we present a method based on self-organizing neural networks to extract the shape of a 2D or 3D object using a set of transformations expressed as versors in the conformal geometric algebra framework. Such transformations, when applied to any geometric entity of this geometric algebra, define the shape of the object. This approach was tested with several images, but here we show its utility first using a 2D magnetic resonance image to segment the ventricle. Then we present some examples of an application for the case of 3D objects.