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)
Neural maps and topographic vector quantization
Neural Networks
Self-Organizing Maps
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Automatic landmark extraction from image data using modified growing neural gas network
IEEE Transactions on Information Technology in Biomedicine
Nonparametric modelling and tracking with active-GNG
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Real-time hand gesture detection and recognition using boosted classifiers and active learning
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Real-time hand detection and tracking using LBP features
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
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Recovering the shape of a class of objects requires establishing correct correspondences between manually or automatically annotated landmark points. In this study, we utilise a novel approach to automatically 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. To measure the quality of the mapping throughout the adaptation process we use the topographic product. Results are given for the training set of hand outlines.