Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
GTM: the generative topographic mapping
Neural Computation
Virtual Human Representation and Communication in VLNet
IEEE Computer Graphics and Applications
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An evaluation of a cost metric for selecting transitions between motion segments
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Automated derivation of behavior vocabularies for autonomous humanoid motion
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Action synopsis: pose selection and illustration
ACM SIGGRAPH 2005 Papers
Locally Linear Embedding for Markerless Human Motion Capture Using Multiple Cameras
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
LLE based gait analysis and recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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Due to the high-dimensionality of motion captured data which resulted in the complexity in motion analysis, a method of motion data processing based on manifold learning was proposed. Isomap, a classical manifold learning algorithm, was necessary to be improved and extended in this paper. A framework of motion data processing based on manifold learning was built to embed high-dimensionality data into low-dimensionality space. It simplified the motion analysis, and in the same time preserved the original motion features. In order to solve the inefficiency of processing large-scale motion data, Sample Isomap (S-Isomap) algorithm was proposed. Experiments proved that approximate embeddings of motion data computed by S-Isomap were average 10 times faster than by Isomap, while 10% frame samples were selected.