Topology representing networks
Neural Networks
Self-organizing maps
Mixtures of probabilistic principal component analyzers
Neural Computation
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Keyframe control of smoke simulations
ACM SIGGRAPH 2003 Papers
Animating pictures with stochastic motion textures
ACM SIGGRAPH 2005 Papers
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
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Matrix neural gas has been proposed as a mathematically well-founded extension of neural gas networks to represent data in terms of prototypes and local principal components in a smooth way. The additional information provided by local principal directions can directly be combined with charting techniques such that a nonlinear embedding of a data manifold into low dimensions results for which an explicit function as well as an approximate inverse exists. In this paper, we show that these ingredients can be used to embed dynamic textures in low dimensional spaces such that, together with a traversing technique in the low dimensional representation, efficient dynamic texture synthesis can be obtained.