Smoothing and matching of 3-D space curves
International Journal of Computer Vision
Robot programming by demonstration (RPD): supporting the induction by human interaction
Machine Learning - Special issue on robot learning
Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision
Numerically Invariant Signature Curves
International Journal of Computer Vision
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
On Using Functions to Describe the Shape
Journal of Mathematical Imaging and Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Statistical Gesture Recognition Through Modelling of Parameter Trajectories
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Hand Gesture using Fourier Descriptors
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Activity Recognition Based on Multiple Motion Trajectories
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Extraction and Temporal Segmentation of Multiple Motion Trajectories in Human Motion
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Elastic Translation Invariant Matching of Trajectories
Machine Learning
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elastic partial matching of time series
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wavelet descriptor of planar curves: theory and applications
IEEE Transactions on Image Processing
Invariant matching and identification of curves using B-splines curve representation
IEEE Transactions on Image Processing
Motion trajectory reproduction from generalized signature description
Pattern Recognition
Probabilistic cluster signature for modeling motion classes
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Atlas-Based probabilistic fibroglandular tissue segmentation in breast MRI
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Motion trajectory is a meaningful and informative clue in characterizing the motions of human, robots or moving objects. Hence, it is important to explore effective motion trajectory modeling. However, with the existing methods, a motion trajectory is used in its raw data form and effective trajectory description is lacking. In this paper, we propose a novel 3D motion trajectory signature descriptor and develop three signature descriptions for motion characterization. The flexible descriptions give the signature high functional adaptability to meet various application requirements in trajectory representation, perception and recognition. The full signature, optimized signature and cluster signature are firstly defined for trajectory representation. Then we explore the motion perception from a single signature, inter-signature matching and the generalization of a cluster signature. Furthermore, three solutions for signature recognition are investigated corresponding to different signature descriptions. The conducted experiments verified the signature's capabilities and flexibility. The signature's application to robot learning is also discussed.