Hybrid GNG Architecture Learns Features in Images
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Learning Topologic Maps with Growing Neural Gas
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Adaptive representation of objects topology deformations with growing neural gas
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Surveillance and human-computer interaction applications of self-growing models
Applied Soft Computing
Video and image processing with self-organizing neural networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Growing neural gas for vision tasks with time restrictions
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Characterization and synthesis of objects using growing neural gas
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Measuring GNG topology preservation in computer vision applications
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
Convexity local contour sequences for gesture recognition
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A self-organizing map for traffic flow monitoring
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
One-shot learning gesture recognition from RGB-D data using bag of features
The Journal of Machine Learning Research
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In this paper we present a new structure capable of characterizing hand posture, as well as its movement. Topology of a self-organizing neural network determines posture, whereas its adaptation dynamics throughout time determines gesture. This adaptive character of the network allows us to avoid the correspondence problem of other methods, so that the gestures are modelled by the movement of the neurons. To validate this method, we have trained the system with 12 gestures, some of which are very similar, and have obtained high success rates (over 97%). This application of a self-organizing network opens up a new field of research because its topology is used to characterize the objects and not to classify them, as is usually the case.