Topology conserving mappings for learning motor tasks
AIP Conference Proceedings 151 on Neural Networks for Computing
Topology representing networks
Neural Networks
Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Self-Organizing Maps
Automatic landmarking of 2d medical shapes using the growing neural gas network
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Automatic landmark extraction from image data using modified growing neural gas network
IEEE Transactions on Information Technology in Biomedicine
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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.