Topology representing networks
Neural Networks
Signal Processing - Video segmentation for content-based processing manipulation
Improved Representation-burden Conservation Network for LearningNon-stationary VQ
Neural Processing Letters
A Novel Self-Creating Neural Network for Learning Vector Quantization
Neural Processing Letters
Tracking and modeling people in video sequences
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Self-Organizing Maps
The LBG-U Method for Vector Quantization – an Improvement over LBGInspired from Neural Networks
Neural Processing Letters
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Hand gesture recognition via a new self-organized neural network
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Least squares quantization in PCM
IEEE Transactions on Information Theory
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image vector quantization algorithm via honey bee mating optimization
Expert Systems with Applications: An International Journal
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
A growing neural gas algorithm with applications in hand modelling and tracking
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms
Neural Processing Letters
Vector quantization using the firefly algorithm for image compression
Expert Systems with Applications: An International Journal
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
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
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In this paper, an original method extended from growing neural gas (GNG-T) [B. Fritzke, A growing neural gas network learns topologies, in: G. Tesauro, D.S. Touretzky, T.K. Leen (Eds.), Advances in Neural Information Processing Systems 7, MIT Press, Cambridge, MA, 1995, pp. 625-632] is presented. The method performs continuously vector quantization over a distribution that changes over time. It deals with both sudden changes and continuous ones, and is thus suited for the video tracking framework, where continuous tracking is required as well as fast adaptation to incoming and outgoing people. The central mechanism relies on the management of the quantization resolution, that copes with stopping condition problems of usual GNG inspired methods. Application to video tracking is presented.